Research on evidence theory decision tree adaptive website

Authors:
Fang Li;Bing Yang;Yi-Yuan Li
Affiliations:
College of computer science and control, Guili university of electronic technology, Guilin;College of computer science and control, Guili university of electronic technology, Guilin;College of computer science and control, Guili university of electronic technology, Guilin
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 5
Cited 0

PARSIMONY: An infrastructure for parallel multidimensional analysis and data mining

Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals

Data Mining and Knowledge Discovery
Efficient Parallel Classification Using Dimensional Aggregates

Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
Reducing Uncertainties in Data Mining

APSEC '97 Proceedings of the Fourth Asia-Pacific Software Engineering and International Computer Science Conference
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques

Quantified Score

Hi-index	0.00

Visualization

Abstract

To meet the personalized needs of web user, a new method combine decision tree base on WEB log with multidimensional data and evidence theory is proposed. This paper first generalizes the concept hierarchies in uncertain database, and then extends the decision tree technique to an uncertain environment where the uncertain is represented by evidence theory. This evidence theory concept hierarchy decision tree algorithm is a new classification method adapt to deal with large-scale uncertain data which come from WEB log, show good accurately performance and good interact performance and good adaptively performance.