Effective Decision Tree Algorithm For Reality Mining - Tapa blanda

Rameshkumar, K.; Sharma, Vidita

 
9783659516276: Effective Decision Tree Algorithm For Reality Mining
Tapa blanda
ISBN 10: 3659516279 ISBN 13: 9783659516276
Editorial: LAP LAMBERT Academic Publishing, 2014

Sinopsis

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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Reseña del editor

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

Biografía del autor

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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Editorial
LAP LAMBERT Academic Publishing
Año de publicación
2014
Idioma
Inglés
ISBN 10 
3659516279
ISBN 13 
9783659516276
Encuadernación
Tapa blanda
Número de páginas
68
Contacto del fabricante
no disponible
Persona responsable
no disponible