Articoli correlati a Effective Decision Tree Algorithm For Reality Mining
This project work deals with reality mining and decision tree. Reality mining is the collection and analysis of data where human social behavior is analyzed through machine-sensed environment, with the goal of identifying predictable patterns of behavior. Classification is the process of finding a model that describe and distinguishes data classes, with the purpose of using model to predict the class of objects whose class label is unknown. A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. ID3 is mathematical algorithm for building the decision tree. It builds the tree from the top down recursive divide-and-conquer manner, with no backtracking. Advantages of ID3 are it build fast and short tree. Disadvantage is data may be over fitted and over classified if a small sample is tested. Only one attribute at a time is tested for making decision. This project work:- To study the drawback of existing decision tree algorithms. To compare the decision tree with R using existing implementation. To apply and study the decision tree with reality mining
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L'autore:
He has awarded PhD CSE from Alagappa University, India. He is working as Associate Professor at Department of Information Technology, Hindustan University, Chennai, India. His area of Interests are BigData, Larger Scale Algorithms and Cloud Data Management.
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- EditoreLAP LAMBERT Academic Publishing
- Data di pubblicazione2014
- ISBN 10 3659516279
- ISBN 13 9783659516276
- RilegaturaCopertina flessibile
- Numero di pagine68
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Descrizione libro Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This project work deals with reality mining and decision tree. Reality mining is the collection and analysis of data where human social behavior is analyzed through machine-sensed environment, with the goal of identifying predictable patterns of behavior. Classification is the process of finding a model that describe and distinguishes data classes, with the purpose of using model to predict the class of objects whose class label is unknown. A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. ID3 is mathematical algorithm for building the decision tree. It builds the tree from the top down recursive divide-and-conquer manner, with no backtracking. Advantages of ID3 are it build fast and short tree. Disadvantage is data may be over fitted and over classified if a small sample is tested. Only one attribute at a time is tested for making decision. This project work:- To study the drawback of existing decision tree algorithms. To compare the decision tree with R using existing implementation. To apply and study the decision tree with reality mining 68 pp. Englisch. Codice articolo 9783659516276
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Effective Decision Tree Algorithm For Reality Mining
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Descrizione libro Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This project work deals with reality mining and decision tree. Reality mining is the collection and analysis of data where human social behavior is analyzed through machine-sensed environment, with the goal of identifying predictable patterns of behavior. Classification is the process of finding a model that describe and distinguishes data classes, with the purpose of using model to predict the class of objects whose class label is unknown. A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. ID3 is mathematical algorithm for building the decision tree. It builds the tree from the top down recursive divide-and-conquer manner, with no backtracking. Advantages of ID3 are it build fast and short tree. Disadvantage is data may be over fitted and over classified if a small sample is tested. Only one attribute at a time is tested for making decision. This project work:- To study the drawback of existing decision tree algorithms. To compare the decision tree with R using existing implementation. To apply and study the decision tree with reality mining. Codice articolo 9783659516276
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Effective Decision Tree Algorithm For Reality Mining
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Descrizione libro PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L0-9783659516276
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Descrizione libro Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Rameshkumar K.He has awarded PhD CSE from Alagappa University, India. He is working as Associate Professor at Department of Information Technology, Hindustan University, Chennai, India. His area of Interests are BigData, Larger Scale. Codice articolo 5161686
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