Top-Down Mining of Interesting Patterns from Very High Dimensional Data

Authors:
Hongyan Liu;Jiawei Han;Dong Xin;Zheng Shao
Affiliations:
Tsinghua University;University of Illinois at Urbana-Champaign;University of Illinois at Urbana-Champaign;University of Illinois at Urbana-Champaign
Venue:
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Year:
2006

Citing 0
Cited 2

Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Are zero-suppressed binary decision diagrams good for mining frequent patterns in high dimensional datasets?

AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many real world applications deal with transactional data, characterized by a huge number of transactions (tuples) with a small number of dimensions (attributes). However, there are some other applications that involve rather high dimensional data with a small number of tuples. Examples of such applications include bioinformatics, survey-based statistical analysis, text processing, and so on. High dimensional data pose great challenges to most existing data mining algorithms. Although there are numerous algorithms dealing with transactional data sets, there are few algorithms oriented to very high dimensional data sets with a relatively small number of tuples.