What is AUDIO MINING? What does AUDIO MINING mean? AUDIO MINING meaning - AUDIO MINING definition - AUDIO MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Audio mining is a technique by which the content of an audio signal can be automatically analysed and searched. It is most commonly used in the field of automatic speech recognition, where the analysis tries to identify any speech within the audio. The audio will typically be processed by a speech recognition system in order to identify word or phoneme units that are likely to occur in the spoken content. This information may either be used immediately in pre-defined searches for keywords or phrases (a real-time "word spotting" system), or the output of the speech recogniser may be stored in an index file. One or more audio mining index files can then be loaded at a later date in order to run searches for keywords or phrases. The results of a search will normally be in terms of hits, which are regions within files that are good matches for the chosen keywords. The user may then be able to listen to the audio corresponding to these hits in order to verify if a correct match was found. Audio mining systems used in the field of speech recognition are often divided into two groups: those that use Large Vocabulary Continuous Speech Recognisers (LVCSR) and those that use phonetic recognition. Musical audio mining (also known as music information retrieval) relates to the identification of perceptually important characteristics of a piece of music such as melodic, harmonic or rhythmic structure. Searches can then be carried out to find pieces of music that are similar in terms of their melodic, harmonic and/or rhythmic characteristics.
Views: 328 The Audiopedia
Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 144222 SciShow
Experts weigh in on the scary data mining happening in Common Core. This brief video clip has been uploaded, and is being used, for non-profit, educational use, or for the purpose of criticism, comment, and news reporting, in accordance with 17 USC § 107 - Limitations on exclusive rights: Fair use: "Notwithstanding the provisions of sections 106 and 106A, the fair use of a copyrighted work, including such use by reproduction in copies or phonorecords or by any other means specified by that section, for purposes such as criticism, comment, news reporting, teaching (including multiple copies for classroom use), scholarship, or research, is not an infringement of copyright. In determining whether the use made of a work in any particular case is a fair use the factors to be considered shall include— "(1) the purpose and character of the use, including whether such use is of a commercial nature or is for nonprofit educational purposes; "(2) the nature of the copyrighted work; "(3) the amount and substantiality of the portion used in relation to the copyrighted work as a whole; and "(4) the effect of the use upon the potential market for or value of the copyrighted work." This brief video clip, from a Cable TV program that runs for two hours or more, is being used for non-profit educational and commentary use, its uploading, embedding, and viewing is clearly in accordance with 17 USC § 107 - Limitations on exclusive rights: Fair use, and the copyright of the creator of the video contained in this short clip remains intact, undiluted, and without violation or damage of any kind.
Views: 9393 Jared Law
For more information: https://www.bitcoinmining.com and https://www.weusecoins.com What is Bitcoin Mining? Have you ever wondered how Bitcoin is generated? This short video is an animated introduction to Bitcoin Mining. Credits: Voice - Chris Rice (www.ricevoice.com) Motion Graphics - Fabian Rühle (www.fabianruehle.de) Music/Sound Design - Christian Barth (www.akkord-arbeiter.de) Andrew Mottl (www.andrewmottl.com)
Views: 6737945 BitcoinMiningCom
Album: Revolution Dominion Composed By: Kaveh Cohen, Michael Nielsen Official: http://ninjatracks.com/ Soundcloud: https://soundcloud.com/ninjatracks Facebook: https://www.facebook.com/ninjatracks Playlist: https://www.youtube.com/playlist?list=PLlbnzwCkgkTB7xBdLOfwPt4vXghX8mzQD Image Albums:- http://theprimes1.imgur.com/ http://theprimes2.imgur.com/ http://theprimes3.imgur.com/ http://theprimes4.imgur.com/ http://theprimes5.imgur.com/ http://theprimes6.imgur.com/ http://theprimes7.imgur.com/ http://theprimes8.imgur.com/ http://theprimes9.imgur.com/ http://theprimes10.imgur.com/ http://theprimes11.imgur.com/ http://theprimes12.imgur.com/ http://theprimes13.imgur.com/ http://theprimes14.imgur.com/ http://theprimes15.imgur.com/ (New Images Goes Here) Additional Links in channel description. Main Image Source: http://wallbase.cc Note for the new Artists: If you would like to submit your own track or Artwork then please follow below instructions. You can either send me an message in here with download link or you can email me at [email protected] - If it's a Track then it needs to be at least 320 kbps with small description/Genre on the track. - If it's a Artwork then it always needs to be 16:9 aspect ratio (No logos in bottom corners) Make sure you subscribe to these two channels. Backup Channel: http://goo.gl/P5T9gI One Hour Station: http://goo.gl/44ZRnG Note:- I'm not the creator of this Music or Image, All rights belongs to respective owners. Feel free to Message me if you know the original Image Artist. This video is purely fan-made, it's done for entertainment purposes only. Have Fun! ▂▂▂▂▂▂▂▂▂▂▂▂▂ Note for the new Artists: ✖✖ If you would like to submit your track, visual art for promotion. ✖✖ If you want to add any kind of information which belongs to the video (audio or visual) ✖✖ If you have any issues regarding any of the videos. Please look for my email address in my channel's about page, please do not send me message on this channel. ▂▂▂▂▂▂▂▂▂▂▂▂▂ Submission Requirements: ✖✖ Audio - Please specify the genre on submission ✖✖ Please provide all your social media links for description. ✖✖ Audio - Must be minimum 320kbps ▂▂▂▂▂▂▂▂ Copyright Info © ✔ Be aware all music and pictures belongs to the original artists. ✔ I am in no position to give anyone permission to use this.
Views: 43083 ThePrimeCronus
23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 448575 Brandon Weinberg
Learn Python here: https://courses.learncodeonline.in/learn/Python3-course In this video, we will talk about basics of web scraping using python. This is a video for total beginners, please comment if you want more videos on web scraping fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com Download LearnCodeOnline.in app from Google play store and Apple App store
Views: 149328 Hitesh Choudhary
The video is giving details about research software developed using WEKA (Open source Data Mining tool) and JAVA (Programming Language). The first version is developed in 2017. Anyone having the link can download this software and directly use this software without any installation. All the instructions are given in 'README.txt' file in a downloaded zip folder. Any suggestions and questions are invited in the comment section below. Feel free to add below. Music Credits: Youtube Audio Library
Views: 76 Prabhjot Kaur
Data Analytics for Beginners -Introduction to Data Analytics https://acadgild.com/big-data/data-analytics-training-certification?utm_campaign=enrol-data-analytics-beginners-THODdNXOjRw&utm_medium=VM&utm_source=youtube Hello and Welcome to data analytics tutorial conducted by ACADGILD. It’s an interactive online tutorial. Here are the topics covered in this training video: • Data Analysis and Interpretation • Why do I need an Analysis Plan? • Key components of a Data Analysis Plan • Analyzing and Interpreting Quantitative Data • Analyzing Survey Data • What is Business Analytics? • Application and Industry facts • Importance of Business analytics • Types of Analytics & examples • Data for Business Analytics • Understanding Data Types • Categorical Variables • Data Coding • Coding Systems • Coding, coding tip • Data Cleaning • Univariate Data Analysis • Statistics Describing a continuous variable distribution • Standard deviation • Distribution and percentiles • Analysis of categorical data • Observed Vs Expected Distribution • Identifying and solving business use cases • Recognizing, defining, structuring and analyzing the problem • Interpreting results and making the decision • Case Study Get started with Data Analytics with this tutorial. Happy Learning For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 239404 ACADGILD
This video helps you learn how to get data from YouTube using graph api v3 Step 1: Create project in developer console using below link https://console.developers.google.com Step 2: Get api key using below link after creating credentials https://console.developers.google.com/apis/credentials Step 3: Retrieve data from YouTube using below links https://www.googleapis.com/youtube/v3/activities?part=snippet,contentDetails&channelId=Channel_id&key=API Key&maxResults=50 https://www.googleapis.com/youtube/v3/commentThreads?part=snippet&allThreadsRelatedToChannelId=Channel_id&key=YOUR_API_KEY https://www.googleapis.com/youtube/v3/commentThreads?part=snippet&videoId=Video_id&key=YOUR_API_KEY
Views: 51075 IGT : INDIVIDUALS GOT TALENT
In this video, I'll guide you how to use WEKA software for preprocessing, classifying, clustering, association. WEKA is a collection of machine learning algorithms for performing data mining tasks. #RanjiRaj #WEKA #DataMining Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj Get WEKA from here : http://www.cs.waikato.ac.nz/ml/weka/
Views: 17615 Ranji Raj
Topic described here are: Multimedia datamining Ubiquitous datamining Distributed datamining Spatial datamining Time series datamining Text mining Video mining Image mining Audio mining multimedia issues Submitted by: A. Vaishnavi II-Msc cs A (project 2) 175214141
Views: 17 vaishu raj
Part 1 of 2: Dr. Karianne Bergen, Harvard Data Science Initiative Fellow at Harvard U., presents "Big data for small earthquakes: a data mining approach to large-scale earthquake detection" at the MIT Earth Resources Laboratory on September 28, 2018. "Earthquake detection, the problem of extracting weak earthquake signals from continuous waveform data recorded by sensors in a seismic network, is a critical and challenging task in seismology. New algorithmic advances in “big data” and artificial intelligence have created opportunities to advance the state-of-the-art in earthquake detection algorithms. In this talk, I will present Fingerprint and Similarity Thresholding (FAST; Yoon et al, 2015), a data mining approach to large-scale earthquake detection, inspired by technology for rapid audio identification. FAST leverages locality sensitive hashing (LSH), a technique for efficiently identifying similar items in large data sets, to detect new candidate earthquakes without template waveforms ("training data"). I will present recent algorithmic extensions to FAST that enable detection over a seismic network and limit false detections due to local correlated noise (Bergen & Beroza, 2018). Using the foreshock sequence prior to the 2014 Mw 8.2 Iquique earthquake as a test case, we demonstrate that our approach is sensitive and maintains a low false detections rate, identifying five times as many events as the local seismicity catalog with a false discovery rate of less than 1%. We show that our new optimized FAST software is capable of discovering new events with unknown sources in 10 years of continuous data (Rong et al, 2018). I will end the talk with recommendations, based on our experience developing the FAST detector, for how the solid Earth geoscience community can leverage machine learning and data mining to enable data-driven discovery. "
Views: 93 MIT Earth Resources Laboratory
DATA MINING COMPUTATIONAL SCIENCE TELKOM UNIVERSITY KELOMPOK 12 audio : Cash Cash - Surrender
Views: 1586 Larita Ditakristy
Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 24588 DataCamp
The video illustrates how text mining techniques allow the analysis of text written in natural language, in order to detect semantic relationships and enable text classification. Audio in Italian. English subtitles available. Illustrations developed by Monica Franceschini, Solution Architecture Manager, Big Data & Analytics Competency Center, Engineering Group.
Views: 382 ItalyMadeOpenSource
Computer Education for all provides complete lectures series on Data Structure and Applications which covers Introduction to Data Structure and its Types including all Steps involves in Data Structures:- Data Structure and algorithm Linear Data Structures and Non-Linear Data Structure on Stack Data Structure on Arrays Data Structure on Queue Data Structure on Linked List Data Structure on Tree Data Structure on Graphs Abstract Data Types Introduction to Algorithms Classifications of Algorithms Algorithm Analysis Algorithm Growth Function Array Operations Two dimensional Arrays Three Dimensional Arrays Multidimensional arrays Matrix operations Operations on linked lists Applications of linked lists Doubly linked lists Introductions to stacks Operations on stack Array based implementation of stack Queue Data Structures Operations on Queues Linked list based implementation of queues Application of Trees Binary Trees Types of Binary Trees Implementation of Binary Trees Binary Tree Traversal Preorder Post order In order Binary Search Tree Introduction to Sorting Analysis of Sorting Algorithms Bubble Sort Selection Sort Insertion Sort Shell Sort Heap Sort Merge Sort Quick Sort Applications of Graphs Matrix representation of Graphs Implementations of Graphs Breadth First Search Topological Sorting Subscribe for More https://www.youtube.com/channel/UCiV37YIYars6msmIQXopIeQ Find us on Facebook: https://web.facebook.com/Computer-Education-for-All-1484033978567298 Java Programming Complete Tutorial for Beginners to Advance | Complete Java Training for all https://youtu.be/gg2PG3TwLx4
Views: 559870 Computer Education For all
EDIT: UE4 Version 4.20 is much better for doing this, use 4.20 and NOT 4.16 instead. DATAMINER DOWNLOAD - http://www.gildor.org/en/projects/umodel WHERE TO LOCATE FILES - C:\Program Files\Epic Games\Fortnite AES KEY V6.10 - 0x47C3245CFAB0F785D4DB3FA8E9967F887ECD623FA51308F1BD6BDB58FCFC6583 REDDIT LINK - https://www.reddit.com/r/FortNiteBR/comments/8ijkll/how_to_datamine_fortnite/ If you enjoyed this video and want to see more guides on fortnite, go ahead and drop this video a thumbs up, and leave a comment asking what you want me to cover next. This video is going to go over how to datamine fortnite, and is a definitive guide of datamining fortnite.
Views: 60140 Vercyx
I just wanted a P.T. video on my channel. All of the vocal audio from PT in-game (not including all the baby sounds (as... Well, they're baby sounds) or the son speeches (since those are in video files apparently and not in-game audio), but everything else), from the radio host to the talking bloody paper bag to Lisa to the radio decoded messages. Shame Silent Hills never happened. This audio wasn't ripped by me, it was ripped by a movement to datamine the P.T. PS4 content over at FacePunch, I just compiled this video and got all the vocal audio together, can see the data mining here: https://facepunch.com/showthread.php?t=1463516
Views: 60370 AestheticGamer
This course aims to introduce advanced database concepts such as data warehousing, data mining techniques, clustering, classifications and its real time applications. SlideTalk video created by SlideTalk at http://slidetalk.net, the online solution to convert powerpoint to video with automatic voice over.
Views: 4185 SlideTalk
Check out our sponsor at http://www.voodoo-realm.com Wowhead: https://www.wowhead.com/news=288097/world-of-warcraft-classic-patch-1-13-build-28211 MMO-Champion: https://www.mmo-champion.com/content/8092-World-of-Warcraft-Classic-Patch-1-13-0-Build-28211 We all speculated that patch 1.13 would be a thing, and it looks like it's happening! Classic UI/Addons: https://youtu.be/6VoRq65-gjI Classic WoW Dungeon Quest Cheat Sheet: https://goo.gl/FMw9XM ○○○ Support me while shopping at Amazon! ○○○ Amazon US: http://amzn.to/2qBLS23 Microphone: http://amzn.to/2asAEUA Video editing software: http://amzn.to/2ANxZpO Video/audio recording software: https://obsproject.com/ Production Music courtesy of Epidemic Sound: http://www.epidemicsound.com
Views: 6866 SoupaSoka
Web Mining Web Mining is the use of Data mining techniques to automatically discover and extract information from World Wide Web. There are 3 areas of web Mining Web content Mining. Web usage Mining Web structure Mining. Web content Mining Web content Mining is the process of extracting useful information from content of web document.it may consists of text images,audio,video or structured record such as list & tables. screen scaper,Mozenda,Automation Anywhere,Web content Extractor, Web info extractor are the tools used to extract essential information that one needs. Web Usage Mining Web usage Mining is the process of identifying browsing patterns by analysing the users Navigational behaviour. Techniques for discovery & pattern analysis are two types. They are Pattern Analysis Tool. Pattern Discovery Tool. Data pre processing,Path Analysis,Grouping,filtering,Statistical Analysis, Association Rules,Clustering,Sequential Pattterns,classification are the Analysis done to analyse the patterns. Web structure Mining Web structure Mining is a tool, used to extract patterns from hyperlinks in the web. Web structure Mining is also called link Mining. HITS & PAGE RANK Algorithm are the Popular Web structure Mining Algorithm. By applying Web content mining,web structure Mining & Web usage Mining knowledge is extracted from web data.
Views: 21478 IT Miner - Tutorials,GK & Facts
This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2). Scales of Measurement Nominal, Ordinal, Interval, Ratio YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td Video Transcript: In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.
Views: 337139 Quantitative Specialists
International Journal of Data Mining & Knowledge Management Process (IJDKP) ISSN : 2230 - 9608 [Online] ; 2231 - 007X [Print] http://airccse.org/journal/ijdkp/ijdkp.html Call for papers :- Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Topics of interest include, but are not limited to, the following: Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 151 aircc journal
A version of this video with closed captions can be accessed at https://youtu.be/tmV2HZF4i1Y. In this webinar hosted by James Bell Associates on May 18, 2017, Dr. Dana Weiner discusses strategies for mining administrative data for the purpose of assessing the characteristics and needs of at-risk child welfare populations. Using examples from a federal Permanency Innovations Initiative (PII) grantee in Illinois, Dr. Weiner identifies the key requirements of productive data mining, steps in the data mining process, and useful statistical techniques for analyzing and making sense of administrative data. We accept comments in the spirit of our comment policy: https://www.hhs.gov/web/socialmedia/policies/comment-policy.html
Views: 23 usgovACF
En esta época mucha de la información que tenemos es digital, todos eso datos nos cuentan historias y nos dan a entender mejor al mundo ya que ocultan un patrón que nos puede revelar algo que no sabíamos... pero como revelamos ese patron? Ayúdame en Patreon: https://goo.gl/GYb3Jj Invítame un café: ko-fi.com/mindmachinetv ====================================================== Redes Sociales: Twitter: https://goo.gl/LNyICo Facebook:https://goo.gl/lcb4Ab Instagram: https://goo.gl/fmLa4J ====================================================== Fuentes que hicieron posible este video: Data mining concepts and techniques: http://myweb.sabanciuniv.edu/rdehkharghani/files/2016/02/The-Morgan-Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline-Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition-Morgan-Kaufmann-2011.pdf ====================================================== Programas que utilizo: Adobe after effects Adobe illustrator Ableton Live 9 Equipo que utilizo: Huion 680s Audio-Technica ATR2500-USB ====================================================== Musica: https://soundcloud.com/musicadfondo Descarga fondos: http://mindmachinetv.tumblr.com/
Views: 7991 MindMachineTV
This 's part of a research project that 's been done by Monash University, Caulfield DSSE. The Idea is to use the voice to send feedback to the user in order to give more accurate information with less energy consumption and less clutter
Views: 406 altaiar22
SUBSCRIBE! Never Miss A Video: https://youtu.be/8Av-drqk6v8 If you liked the video please remember to leave a Like & Comment, I appreciate it a lot Source: https://www.reddit.com/r/thedivision/comments/8r45by/datamining_list_of_all_shields_patches/ Check out Kontrol Freek: https://kontrolfreek.pxf.io/c/1151492/367518/5408 Use code "JustForFunCJ" and get 10% off on any thing you want. Get The Division Game http://weav.me/~k5fq Patreon Page link : https://www.patreon.com/J4FwithCJ TWITTER: https://twitter.com/J4FwithCJ Twitch: https://www.twitch.tv/j4fwithcj Facebook page : https://www.facebook.com/Just4FunWithCJ
Views: 6203 Just For Fun
Lessons learned from establishing Data Analytics initiative in Danfoss leveraging advanced analytics techniques as well data mining and visualization for optimization and innovation. Learning points: - Meeting your stakeholders at eye level: Power of the 3 C's Concepts, Capabilities, Culture - Fail Fast, be flexible and iterate - Presentation of use cases with applied data analytics and mining techniques for both internal and external stakeholders #HyperightDataTalks is a video podcast of interviews with some of the most innovative minds, enterprise practitioners, technology and service providers, start-ups and academics, working with Data Science, Data Management, Big Data, Analytics, AI, IOT and much more. For more interviews, audio podcast and videos from some of the best presentations from our Data Summits, please visit www.hyperight.com Presentation recorded during Maintenance Analytics Summit 2018 - https://maintenanceanalyticssummit.com/ Follow us on Twitter: https://twitter.com/PdMASummit
Views: 37 Hyperight AB
This is the first chapter in the web lecture series of Prof. dr. Bart Baesens: Introduction to Database Management Systems. Prof. dr. Bart Baesens holds a PhD in Applied Economic Sciences from KU Leuven University (Belgium). He is currently an associate professor at KU Leuven, and a guest lecturer at the University of Southampton (United Kingdom). He has done extensive research on data mining and its applications. For more information, visit http://www.dataminingapps.com In this lecture, the fundamental concepts behind databases, database technology, database management systems and data models are explained. Discussed topics entail: applications, definitions, file based vs. databased data management approaches, the elements of database systems and the advantages of database design.
Views: 301888 Bart Baesens
• Follow me on Twitter: https://mobile.twitter.com/Loves2smasH • • Click here for my other Street Fighter videos: https://www.youtube.com/playlist?list=PLXol2BXy1R_Ni1yIB2gtlBZNEJPK-k54I • • SUBSCRIBE for all the Latest Fighting Game News & Speculation • New datamining revealed the 6 DLC characters, Story Dislogue (link in comments also), online ranking names & Capcom Pro Tour events & locations
Views: 4026 Loves Smash
How to create a complete customer portfolio management system for you automotive dealership.
Views: 164 Joey Little
In this episode of Becoming a Data Scientist Podcast, we meet Will Kurt, who talks about his path from English & Literature and Library & Information Science degrees to becoming the Lead Data Scientist at KISSmetrics. He also tells us about his probability blog, Count Bayesie, and I introduce Data Science Learning Club Activity 1. Will has some great advice for people learning data science! Note: The video is the interview portion only. The audio podcast has the intro, interview, and data science learning club activity explanation. More info and podcast link on the Becoming a Data Scientist Blog: http://www.becomingadatascientist.com/2015/12/21/becoming-a-data-scientist-podcast-episode-01-will-kurt/
Views: 8477 Becoming a Data Scientist
Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. According to IBM, 2.5 billion gigabytes (GB) of data was generated every day in 2012. An article by Forbes states that Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet. Which makes it extremely important to at least know the basics of the field. After all, here is where our future lies. In this video, we will differentiate between the Data Science, Big Data, and Data Analytics, based on what it is, where it is used, the skills you need to become a professional in the field, and the salary prospects in each field. For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 170614 Simplilearn
Facebook CEO Mark Zuckerberg will testify today before a U.S. congressional hearing about the use of Facebook data to target voters in the 2016 election. Zuckerberg is expected to offer a public apology after revelations that Cambridge Analytica, a data-mining firm affiliated with Donald Trump's presidential campaign, gathered personal information about 87 million users to try to influence elections. »»» Subscribe to CBC News to watch more videos: http://bit.ly/1RreYWS Connect with CBC News Online: For breaking news, video, audio and in-depth coverage: http://bit.ly/1Z0m6iX Find CBC News on Facebook: http://bit.ly/1WjG36m Follow CBC News on Twitter: http://bit.ly/1sA5P9H For breaking news on Twitter: http://bit.ly/1WjDyks Follow CBC News on Instagram: http://bit.ly/1Z0iE7O Download the CBC News app for iOS: http://apple.co/25mpsUz Download the CBC News app for Android: http://bit.ly/1XxuozZ »»»»»»»»»»»»»»»»»» For more than 75 years, CBC News has been the source Canadians turn to, to keep them informed about their communities, their country and their world. Through regional and national programming on multiple platforms, including CBC Television, CBC News Network, CBC Radio, CBCNews.ca, mobile and on-demand, CBC News and its internationally recognized team of award-winning journalists deliver the breaking stories, the issues, the analyses and the personalities that matter to Canadians.
Views: 131470 CBC News
Should You Buy a Used Mining GPU? - Probing Paul #24 ⇨ Sponsor - Deepcool New Ark 90 | Buy: https://amzn.to/2IeP67Q | Info: http://bit.ly/PH-ARK90 My Wrist Rest (a regular question I get) - http://amzn.to/2bKEEna ►TIMESTAMPS◄ 0:51 Get a Haircut 2:10 Should you buy a used mining GPU? 4:41 Does overclocking degrade components? 6:34 Why are some of your uploads 1080p/60, and some 4K? 8:02 Films look great at 24fps, so why do games suck at 30fps? 11:02 Is Intel 6th Gen (Z270/H270 Motherboards, Skylake CPUs) still a worthwhile investment? 12:46 Dread Pirate Roberts or Inigo Montoya? 13:37 Elevator or Stairs? ▷ MY STORE - shirts, mugs, pint glasses & hoodies http://paulshardware.net ▷ SOCIAL Twitter: @paulhardware http://www.twitter.com/paulhardware Facebook: https://www.facebook.com/pages/Pauls-Hardware/195425877329550 Instagram: http://instagram.com/paulhardware :::Send Me Stuff::: Paul's Hardware P.O. Box 4325 Diamond Bar, CA 91765 ► Edited by Joe Aguilar - ShaostylePostProductions https://www.facebook.com/ShaostylePostProductions/ Audio file(s) provided by Epidemic Sound http://www.epidemicsound.com/
Views: 100334 Paul's Hardware
This video shows the OCR in real time on a bad video quality - #Gridbots #gridEYE #deeplearning
Views: 242 Gridbots Operations
"The National Security Agency and the FBI are tapping directly into the central servers of nine leading U.S. Internet companies, extracting audio and video chats, photographs, e-mails, documents, and connection logs that enable analysts to track foreign targets, according to a top-secret document obtained by The Washington Post."* We now know that the NSA was and is getting phone records from millions of Americans, but the Washington Post has uncovered that the Obama administration is also overseeing data mining of 9 major internet companies under PRISM. A huge amount of personal data is being monitored more closely than most ever thought possible. Is this Obama's "change?" Cenk Uygur breaks it down. *Read more from The Washington Post: http://www.washingtonpost.com/investigations/us-intelligence-mining-data-from-nine-us-internet-companies-in-broad-secret-program/2013/06/06/3a0c0da8-cebf-11e2-8845-d970ccb04497_story.html Support The Young Turks by Subscribing http://www.youtube.com/user/theyoungturks Like Us on Facebook: Follow Us on Twitter: http://www.twitter.com/theyoungturks Support TYT for FREE by doing your Amazon shopping through this link (bookmark it!) http://www.amazon.com/?tag=theyoungturks-20 Buy TYT Merch: http://theyoungturks.spreadshirt.com/ Support The Young Turks by becoming a member of TYT Nation at http://www.tytnetwork.com/member-options/. Your membership supports the day to day operations and is vital for our continued success and growth. In exchange, we provided members only bonuses! We tape a special Post Game show Mon-Thurs and you get access to the entire live show at your convenience in video, audio and podcast formats.
Views: 34828 The Young Turks
This is the first part of a two-part video about setting up a Windows 10 KVM VM in unRAID. The first part deals with setting up the VM then the second part passing through hardware to turn it into a gaming VM. Please, if you can, support the channel and donate https://goo.gl/dw6MLW The first part of these videos you will learn how to: 1. Download a windows 10 iso. https://www.microsoft.com/en-gb/software-download/windows10ISO 2. Where to Buy a license for windows 10 pro reatail for $23 and the oem for $13 Here is a new link where you can buy the windows 10. It seems like the playasia link i had here before have sold out of their windows keys! So here is a new link. I have ordered from these guys before and they seem okay :) https://psngames.org/downloads/microsoft-windows-10-professional/?ref=573 3. How to assign resources and correctly pin your cpus. 4. How to install the virtio drivers including the qxl graphics driver. 5. How to remove or block the windows 10 data mining - phone home - etc with anti beacon. https://www.safer-networking.org/spybot-anti-beacon/ 6. How to install multiple useful programmes with ninite https://ninite.com/ 7. Using Splashtop desktop for good quality remote viewing https://www.splashtop.com/personal 8. How to install a virtual sound card to have sound in Splashtop/RDP etc. 9. Using mapped drives and symlinks to get the most out of the array. dirlink http://dirlinker.codeplex.com/ 10. Windows tweaks to VM compatibility. 11. general tips Thanks to everyone who posted in the forums in my thread for the cpu hyperthreads to be shown in the vm manager. They are now!! Awesome :) Big thanks to the Limetech Team for unRAID Music credits BoxCat Games http://freemusicarchive.org/music/BoxCat_Games/
Views: 58485 Spaceinvader One
Views: 2647516 CuriousInventor
Subscribe to France 24 now: http://f24.my/youtubeEN FRANCE 24 live news stream: all the latest news 24/7 http://f24.my/YTliveEN The CIA-funded data mining firm Palantir Technologies has announced it's partnering up with the World Food Programme to help the relief agency better use its data. Human rights advocates are describing the move as "irresponsible" and "potentially harmful". In this edition, we speak to Enrica Porcari from the WFP. Plus, we tell you more about the battle for 5G dominance. And in Test 24, we test the solar-powered connected camera Tikee by French startup Enlaps. Visit our website: http://www.france24.com Subscribe to our YouTube channel: http://f24.my/youtubeEN Like us on Facebook: https://www.facebook.com/FRANCE24.English Follow us on Twitter: https://twitter.com/France24_en
Views: 536 FRANCE 24 English
IBM Watson Explorer has two primary use cases – Analytics and Search. This video provides a quick tour of Content Miner used by Citizen Analysts and Data Scientist to discover insights from textual data. To learn more about IBM Watson Explorer, please visit: http://ibm.biz/ContentAnalytics
Views: 2526 IBM Analytics
Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 367274 APMonitor.com
Introduction to Spectral Orange, a flavor of Orange for analyzing spectroscopy data. Please note that Collagen spectroscopy data set has been renamed to Liver spectroscopy. Full Quasar package: https://quasar.codes/ Get Orange: https://orange.biolab.si/ See Spectroscopy add-on: https://github.com/markotoplak/orange-infrared License: GNU GPL + CC Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana In collaboration with: Soleil Synchrotron, Elettra Sincrotrone Trieste, BioSpec Norway and Canadian Light Source. Design: Agnieszka Rovšnik Music: THE HAPPY SONG by Nicolai Heidlas Music https://soundcloud.com/nicolai-heidlas Creative Commons — Attribution 3.0 Unported— CC BY 3.0 http://creativecommons.org/licenses/b... Music promoted by Audio Library https://youtu.be/cGuaRsXLScQ
Views: 4484 Orange Data Mining
Author: ChengXiang Zhai, Department of Computer Science, University of Illinois at Urbana-Champaign Chase Geigle, Department of Computer Science, University of Illinois at Urbana-Champaign Abstract: Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people manage and analyze vast amount of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans for humans. This has two consequences. First, since text data are generated by people, they are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. Second, since text is written for consumption by humans, humans play a critical role in any text data application system, and a text management and analysis system must involve them in the loop of text analysis. Existing toolkits supporting text management and analysis tend to fall into two categories. The first is search engine toolkits, which are especially suitable for building a search engine application, but tend to have limited support for text analysis/mining functions. Examples include Lucene, Terrier, and Indri/Lemur. The second is text mining or general data mining and machine learning toolkits, which tend to selectively support some text analysis functions, but generally do not support search capability. However, seamless integration of search engine capabilities with various text analysis functions is necessary due to two reasons. First, while the raw data may be large for any particular problem, it is often a relatively small subset of the data that are relevant, and a search engine is an essential tool for quickly discovering a small subset of relevant text data in a large text collection. Second, search engines are needed to help analysts interpret any patterns discovered in the data by allowing them to examine the relevant original text data to make sense of any discovered pattern. A main design philosophy of MeTA, which also differentiates MeTA from all the existing toolkits, is its emphasis on the tight integration of search capabilities (indeed, text access capabilities in general) with text analysis functions, enabling it to provide full support for building a powerful text analysis application. Another design philosophy of MeTA is to facilitate education and research experiments with various algorithms. In this direction, it is similar to Indri/Lemur in its emphasis on modularity and extensibility achieved through object-oriented design. It enables flexible configuration of a selected subset of modules so as to make it easy for designing course assignments or experimenting with a few selected algorithms as needed in focused research projects. For example, it has been successfully used in a MOOC on Text Retrieval and Search Engines where over one thousand Coursera learners have used the toolkit to finish a large programming assignment. It will be used again for supporting programming assignments for another upcoming MOOC on Text Mining and Analytics. https://meta-toolkit.org/KDD2017 More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 203 KDD2017 video
SQL Server Analysis Services: Data Mining Add-in For Excel See more info, and read the video transcript: http://msdn.microsoft.com/en-us/library/dd744748.aspx
Views: 3935 sqlserver
We are ready to provide guidance to successfully complete your projects and also download the abstract, base paper from our website Note: Voice Video Listen with audio Visit : www.javafirst.in Contact: 73383 45250
Views: 151 Java First IEEE Final Year Projects