Subspace Clustering Based on Compressibility

Authors:
Masaki Narahashi;Einoshin Suzuki
Affiliations:
-;-
Venue:
DS '02 Proceedings of the 5th International Conference on Discovery Science
Year:
2002

Citing 6
Cited 0

BIRCH: an efficient data clustering method for very large databases

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Squashing flat files flatter

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Ubiquitous B-Tree

ACM Computing Surveys (CSUR)
Stochastic Complexity in Statistical Inquiry Theory

Stochastic Complexity in Statistical Inquiry Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a subspace clustering method based on compressibility. It is widely accepted that compressibility is deeply related to inductive learning. We have come to believe that compressibility is promising as an evaluation criterion in subspace clustering, and propose SUBCCOM in order to verify this belief. Experimental evaluation employs both artificial and real data sets.