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Data Mining For Automated Personality Classification
 
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Get this project at http://nevonprojects.com/data-mining-for-automated-personality-classification-2/ Here we use data mining algorithm to mine a training data set for automated human personality classification.
Views: 5375 Nevon Projects
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 40965 Red Apple Tutorials
CS5593 - Data Mining - Credit Card Fraud Detection Using Classification Algorithms
 
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Team Members: Prasanti Vinta Lavanya Saravanan Vinothini Rajasekaran
Smart Health Prediction Using Data Mining
 
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Get the project at http://nevonprojects.com/smart-health-prediction-using-data-mining/ A smart system that suggests a persons disease and suggestions to cure based on his symptoms, also has online doctor to consult for further treatment and cure.
Views: 38435 Nevon Projects
Soil Classification Using Data Mining Techniques: A Comparative Study | Final Year Projects 2016
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 200 Clickmyproject
Intelligent Heart Disease Prediction System Using Data Mining Techniques
 
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We are providing a Final year IEEE project solution & Implementation with in short time. If anyone need a Details Please Contact us Mail: [email protected] or [email protected] Phone: 09842339884, 09688177392 Watch this also: https://www.youtube.com/channel/UCDv0caOoT8VJjnrb4WC22aw ieee projects, ieee java projects , ieee dotnet projects, ieee android projects, ieee matlab projects, ieee embedded projects,ieee robotics projects,ieee ece projects, ieee power electronics projects, ieee mtech projects, ieee btech projects, ieee be projects,ieee cse projects, ieee eee projects,ieee it projects, ieee mech projects ,ieee e&I projects, ieee IC projects, ieee VLSI projects, ieee front end projects, ieee back end projects , ieee cloud computing projects, ieee system and circuits projects, ieee data mining projects, ieee image processing projects, ieee matlab projects, ieee simulink projects, matlab projects, vlsi project, PHD projects,ieee latest MTECH title list,ieee eee title list,ieee download papers,ieee latest idea,ieee papers,ieee recent papers,ieee latest BE projects,ieee B tech projects| Engineering Project Consultants bangalore, Engineering projects jobs Bangalore, Academic Project Guidance for Electronics, Free Synopsis, Latest project synopsiss ,recent ieee projects ,recent engineering projects ,innovative projects| Computer Software Project Management Consultants, Project Consultants For Electrical, Project Report Science, Project Consultants For Computer, ME Project Education Consultants, Computer Programming Consultants, Project Consultants For Bsc, Computer Consultants, Mechanical Consultants, BCA live projects institutes in Bangalore, B.Tech live projects institutes in Bangalore,MCA Live Final Year Projects Institutes in Bangalore,M.Tech Final Year Projects Institutes in Bangalore,B.E Final Year Projects Institutes in Bangalore , M.E Final Year Projects Institutes in Bangalore,Live Projects,Academic Projects, IEEE Projects, Final year Diploma, B.E, M.Tech,M.S BCA, MCA Do it yourself projects, project assistance with project report and PPT, Real time projects, Academic project guidance Bengaluru| Image Processing ieee projects with source code,VLSI projects source code,ieee online projects.best projects center in Chennai, best projects center in trichy, best projects center in bangalore,ieee abstract, project source code, documentation ,ppt ,UML Diagrams,Online Demo and Training Sessions|Data mining, IHDPS, Decision Tree, Neural Network, Naive Bayes
Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 7231 Clickmyproject
Final Year Projects 2015 | Soil Classification Using  Data Mining  Techniques
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 643 myproject bazaar
Final Year Projects 2015 |  Soil Classification Using  Data Mining
 
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Including Packages ===================== * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 1436 Clickmyproject
Soil Classification Using  Data Mining  Techniques | Final Year Projects 2016
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 159 myproject bazaar
A Novel Data Mining Approach for Soil Classification | Final Year Projects 2016 - 2017
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 69 Clickmyproject
KNN Classification||ML Project||Breast Cancer Prediction
 
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This is a project on Breast Cancer Prediction, in which we use the KNN Algorithm for classifying between the Malignant and Benign cases. We are using a Kaggle dataset for executing this task. We are first creating a model, then we create a confusion matrix to check the accuracy of our model. You can download the dataset here: https://www.dropbox.com/s/2qkskdmv7nywv7p/Breast_Cancer.csv?dl=0
Views: 1603 Rajesh Dorbala
Prediction of Student Results #Data Mining
 
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We used WEKA datamining s-w which yields the result in a flash.
Views: 34258 GRIETCSEPROJECTS
Heart Disease Prediction Project
 
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Get this project kit at http://nevonprojects.com/heart-disease-prediction-project/ System allows user to predict heart disease by users symptoms using data mining
Views: 34347 Nevon Projects
Text Categorization and Clustering Data Mining Rapidminer Projects
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-wireless-body-area-network/
Views: 7061 PHD Projects
AN EFFICIENT PREDICTION OF CANCER USING DATA MINING TECHNIQUE
 
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Cancer is one of the major causes of death when compared to all other diseases. Cancer has become the most hazardous types of disease among the living creature in the world. Early detection of cancer is essential in reducing life losses. This work aims to establish an accurate classification model for Cancer prediction, in order to make full use of the invaluable information in clinical data. The dataset is divided into training set and test set. In this experiment, we compare six classification techniques in Weka software and comparison results show that Support Vector Machine (SVM) has higher prediction accuracy than those methods. Different methods for cancer detection are explored and their accuracies are compared. With these results, we infer that the SVM are more suitable in handling the classification problem of cancer prediction, and we recommend the use of these approaches in similar classification problems. This work presents a comparison among the different Data mining classifiers on the database of cancer, by using classification accuracy.
Views: 4861 David Clinton
A Novel Data Mining Approach for Soil Classification | Final Year Projects 2016 - 2017
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 36 myproject bazaar
Gaurang Panchal - Data Mining/Machine Learning Project
 
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Dataset: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing# Overview: The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. This dataset consists of client information of a bank; 41188 records with 20 inputs, ordered by date (from May 2008 to November 2010). Aim: The classification goal is to predict if the client will subscribe (yes/no) a term deposit. The data includes information about the clients and marketing calls. Together with this data there is a record of whether the clients are currently enrolled for a term deposit. All of the variables should be considered and modeled to produce classification to accurately predict an entry for a client. Attribute Information: Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown') # related with the last contact of the current campaign: 8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. # other attributes: 12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success') # social and economic context attributes 16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric) Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')
Views: 599 Gaurang Panchal
CS 5593 Data Mining Project: Classification of Animal Shelter Outcomes
 
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This project was completed as part of the course CS5593: Data Mining at the University of Oklahoma Project Members: Omar Mirza Jingyu Wang Supreetha Akella Praveen Puvvadi
Views: 101 Omar Mirza
Data analytics CRISP-DM Project
 
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Data Analytics Classification Bank Marketing Dataset source UCI machine learning: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing
Views: 395 PARTH PANDYA
Data Mining and Predictive Analytics Final Project
 
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Modeling Analysis (Education Dataset) in SPSS
Views: 95 gong wen
Online News Popularity Demo - Data Mining Project Fall 2015 OU
 
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Demonstration of a project in CS 5593 Data Mining in Fall 2015 at the University of Oklahoma for the Classification of Online News Popularity based on the "Online News Popularity Data Set" in the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Online+News+Popularity). The project was developed by Maxime Brisse, Aitor Algorta and Sven Erik Jeroschewski.
Views: 724 Sven
Cancer Identification Data Mining Java Project
 
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Cancer Identification System Data Mining Java Project Download Project Code, Report and PPT :+91 7702177291, +91 9052016340 Email : [email protected] Website : www.1000projects.org
Views: 2237 1000 Projects
Food Recommendation System using Data Mining and SVM Classification
 
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Food Recommendation System using Data Mining and SVM Classification java projects Latest IEEE Projects 2018-2019 IEEE Java Projects Topics Free IEEE Projects download Thanks & Regards, 1Crore Projects Java IEEE Projects , Head Office: No.68 & 70 ,Ground Floor, Raahat Plaza, Vadapalani,Chennai - 26. Contact: +91 7708150152 , +91 9751800789 Web :- http://1croreprojects.com/ E-Mail ID : [email protected]
Views: 173 1 Crore Projects
Disease Prediction System using Data Mining
 
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Disease Prediction System using Data Mining To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9. Mobile: (0)9952649690, Email: [email protected], Website: https://www.jpinfotech.org The successful application of data mining in highly visible fields like e-business, commerce and trade has led to its application in other industries. The medical environment is still information rich but knowledge weak. There is a wealth of data possible within the medical systems. However, there is a lack of powerful analysis tools to identify hidden relationships and trends in data. Heart disease is a term that assigns to a large number of heath care conditions related to heart. These medical conditions describe the unexpected health conditions that directly control the heart and all its parts. Medical data mining techniques like association rule mining, classification, clustering is implemented to analyze the different kinds of heart based problems. Classification is an important problem in data mining. Given a database contain collection of records, each with a single class label, a classifier performs a brief and clear definition for each class that can be used to classify successive records. A number of popular classifiers construct decision trees to generate class models. The data classification is based on MAFIA algorithms which result in accuracy, the data is estimated using entropy based cross validations and partition techniques and the results are compared. The heart disease database is clustered using the K-means clustering algorithm, which will remove the data applicable to heart attack from the database.
Views: 692 JPINFOTECH PROJECTS
Data Mining Project Presentation
 
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Sem 1 18/19
Views: 59 Amin Fauzi
Data Mining Projects 2016-2017 | ieee data mining papers 2016
 
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ieee data mining papers 2016 for ME,M.Tech.,M.Phil., Ph.D., B.E, B.Tech., MCA A Novel Recommendation Model Regularized with User Trust and Item Ratings Automatically Mining Facets for Queries from Their Search Results Booster in High Dimensional Data Classification Building an intrusion detection system using a filter-based feature selection algorithm Connecting Social Media to E-Commerce: Cold-Start Product Recommendation Using Microblogging Information Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings Crowdsourcing for Top-K Query Processing over Uncertain Data Cyberbullying Detection based on Semantic-Enhanced Marginalized Denoising Auto-Encoder Domain-Sensitive Recommendation with User-Item Subgroup Analysis Efficient Algorithms for Mining Top-K High Utility Itemsets Efficient Cache-Supported Path Planning on Roads Mining User-Aware Rare Sequential Topic Patterns in Document Streams Nearest Keyword Set Search in Multi-Dimensional Datasets Rating Prediction based on Social Sentiment from Textual Reviews Location Aware Keyword Query Suggestion Based on Document Proximity Using Hashtag Graph-based Topic Model to Connect Semantically-related Words without Co-occurrence in Microblogs Quantifying Political Leaning from Tweets, Retweets, and Retweeters Relevance Feedback Algorithms Inspired By Quantum Detection Sentiment Embeddings with Applications to Sentiment Analysis Top-Down XML Keyword Query Processing TopicSketch: Real-time Bursty Topic Detection from Twitter Top-k Dominating Queries on Incomplete Data Understanding Short Texts through Semantic Enrichment and Hashing To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com
Views: 2626 JPINFOTECH PROJECTS
Data mining projects using weka
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-projects-uk/
Views: 3554 PHD Projects
Prediction Analysis of Heart Patients using Naive Bayes and Random Forest Algorithms
 
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Introduction Heart Diseases remain the biggest cause of deaths for the last two epochs. Recently computer technology develops software to assistance doctors in making decision of heart disease in the early stage. Diagnosing the heart disease mainly depends on clinical and obsessive data. Prediction system of Heart disease can assist medical experts for predicting heart disease current status based on the clinical data of various patients. In this project, the Heart disease prediction using classification algorithm Naive Bayes, and Random Forest is discussed. Naive Bayes Algorithm The Naive Bayes classification algorithm is a probabilistic classifier. It is based on probability models that incorporate strong independence assumptions. Naive Bayes is a simple technique for constructing classifiers models that assign class labels to problem instances. It assume that the value of a particular feature is independent of the value of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 10 cm in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of any possible correlations between the color, roundness, and diameter features. Random Forest Technique In this technique, a set of decision trees are grown and each tree votes for the most popular class, then the votes of different trees are integrated and a class is predicted for each sample. This approach is designed to increase the accuracy of the decision tree, more trees are produced to vote for class prediction. This approach is an ensemble classifier composed of some decision trees and the final result is the mean of individual trees results. Follow Us: Facebook : https://www.facebook.com/E2MatrixTrainingAndResearchInstitute/ Twitter: https://twitter.com/e2matrix_lab/ LinkedIn: https://www.linkedin.com/in/e2matrix-thesis-jalandhar/ Instagram: https://www.instagram.com/e2matrixresearch/
Views: 1721 E2MATRIX RESEARCH LAB
Stock market prediction | Data Mining Project
 
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Stock Market prediction using Machine Learning Algorithm. By: Devansh Chauhan Kartik Jain
Views: 931 Devansh Chauhan
Statistical Data Mining Project
 
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Instacart Market Basket analysis : Vineel Patnana, Aditya Sahay
Views: 96 Vineel patnana
Data mining projects for students
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-help/
Views: 814 PHD Projects
Data Mining  Association Rule - Basic Concepts
 
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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Making Predictions with Data and Python : Predicting Credit Card Default | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2eZbdPP]. Demonstrate how to build, evaluate and compare different classification models for predicting credit card default and use the best model to make predictions. • Introduce, load and prepare data for modeling • Show how to build different classification models • Show how to evaluate models and use the best to make predictions For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 32321 Packt Video
Data Mining Project - Analysis on Car Dataset
 
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In this video, I have demonstrated the analysis performed on the car dataset (dataset source: UCI repository) by using SAS Enterprise Miner.
Data Mining Project
 
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Views: 38 chritian458
Crime Data Analysis Using Kmeans Clustering Technique
 
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Introduction Data Mining deals with the discovery of hidden knowledge, unexpected patterns and new rules from large databases. Crime analyses is one of the important application of data mining. Data mining contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications It can help the analysts to identify crimes faster and help to make faster decisions. The main objective of crime analysis is to find the meaningful information from large amount of data and disseminates this information to officers and investigators in the field to assist in their efforts to apprehend criminals and suppress criminal activity. In this project, Kmeans Clustering is used for crime data analysis. Kmeans Algorithm The algorithm is composed of the following steps: It randomly chooses K points from the data set. Then it assigns each point to the group with closest centroid. It again recalculates the centroids. Assign each point to closest centroid. The process repeats until there is no change in the position of centroids. Example of KMEANS Algorithm Let’s imagine we have 5 objects (say 5 people) and for each of them we know two features (height and weight). We want to group them into k=2 clusters. Our dataset will look like this: First of all, we have to initialize the value of the centroids for our clusters. For instance, let’s choose Person 2 and Person 3 as the two centroids c1 and c2, so that c1=(120,32) and c2=(113,33). Now we compute the Euclidean distance between each of the two centroids and each point in the data.
Views: 1443 E2MATRIX RESEARCH LAB
DATA MINING PROJECT 2018
 
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Views: 39 eralorr7
Data Mining Project
 
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Data Mining Project
evaluation of predictive data mining algorithms in soil data classification for optimized crop recom
 
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evaluation of predictive data mining algorithms in soil data classification for optimized crop recom- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project NETWORKING 1. A Non-Monetary Mechanism for Optimal Rate Control Through Efficient Cost Allocation 2. A Probabilistic Framework for Structural Analysis and Community Detection in Directed Networks 3. A Ternary Unification Framework for Optimizing TCAM-Based Packet Classification Systems 4. Accurate Recovery of Internet Traffic Data Under Variable Rate Measurements 5. Accurate Recovery of Internet Traffic Data: A Sequential Tensor Completion Approach 6. Achieving High Scalability Through Hybrid Switching in Software-Defined Networking 7. Adaptive Caching Networks With Optimality Guarantees 8. Analysis of Millimeter-Wave Multi-Hop Networks With Full-Duplex Buffered Relays 9. Anomaly Detection and Attribution in Networks With Temporally Correlated Traffic 10. Approximation Algorithms for Sweep Coverage Problem With Multiple Mobile Sensors 11. Asynchronously Coordinated Multi-Timescale Beamforming Architecture for Multi-Cell Networks 12. Attack Vulnerability of Power Systems Under an Equal Load Redistribution Model 13. Congestion Avoidance and Load Balancing in Content Placement and Request Redirection for Mobile CDN 14. Data and Spectrum Trading Policies in a Trusted Cognitive Dynamic Network Architecture 15. Datum: Managing Data Purchasing and Data Placement in a Geo-Distributed Data Market 16. Distributed Packet Forwarding and Caching Based on Stochastic NetworkUtility Maximization 17. Dynamic, Fine-Grained Data Plane Monitoring With Monocle 18. Dynamically Updatable Ternary Segmented Aging Bloom Filter for OpenFlow-Compliant Low-Power Packet Processing 19. Efficient and Flexible Crowdsourcing of Specialized Tasks With Precedence Constraints 20. Efficient Embedding of Scale-Free Graphs in the Hyperbolic Plane 21. Encoding Short Ranges in TCAM Without Expansion: Efficient Algorithm and Applications 22. Enhancing Fault Tolerance and Resource Utilization in Unidirectional Quorum-Based Cycle Routing 23. Enhancing Localization Scalability and Accuracy via Opportunistic Sensing 24. Every Timestamp Counts: Accurate Tracking of Network Latencies Using Reconcilable Difference Aggregator 25. Fast Rerouting Against Multi-Link Failures Without Topology Constraint 26. FINE: A Framework for Distributed Learning on Incomplete Observations for Heterogeneous Crowdsensing Networks 27. Ghost Riders: Sybil Attacks on Crowdsourced Mobile Mapping Services 28. Greenput: A Power-Saving Algorithm That Achieves Maximum Throughput in Wireless Networks 29. ICE Buckets: Improved Counter Estimation for Network Measurement 30. Incentivizing Wi-Fi Network Crowdsourcing: A Contract Theoretic Approach 31. Joint Optimization of Multicast Energy in Delay-Constrained Mobile Wireless Networks 32. Joint Resource Allocation for Software-Defined Networking, Caching, and Computing 33. Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints 34. Memory-Efficient and Ultra-Fast Network Lookup and Forwarding Using Othello Hashing 35. Minimizing Controller Response Time Through Flow Redirecting in SDNs 36. MobiT: Distributed and Congestion-Resilient Trajectory-Based Routing for Vehicular Delay Tolerant Networks 37. Network-Aware Feasible Repairs for Erasure-Coded Storage 38. Node-Based Distributed Channel Access With Enhanced Delay Characteristics
Views: 18 Micans Infotech
Final Year Projects 2015 | Applying Data Mining Techniques to Address Disaster
 
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Diglett - Data Mining Project
 
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GitHub Development page: https://github.com/dreid93/diglett-4477/tree/develop Live Demo: http://140.184.137.122:1337
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CLUSTERING IN DATA MINING PROJECTS IN BAHRAIN
 
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