SCHISM: A new approach for interesting subspace mining

Karlton Sequeira, Mohammed Zaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

64 Citations (Scopus)

Abstract

High-dimensional data pose challenges to traditional clustering algorithms due to their inherent sparsity and data tend to cluster in different and possibly overlapping subspaces of the entire feature space. Finding such subspaces is called subspace mining. We present SCHISM, a new algorithm for mining interesting subspaces, using the notions of support and Chernoff-Hoeffding bounds. We use a vertical representation of the dataset, and use a depth-first search with backtracking to find maximal interesting subspaces. We test our algorithm on a number of high-dimensional synthetic and real datasets to test its effectiveness.

Original languageEnglish
Title of host publicationProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
EditorsR. Rastogi, K. Morik, M. Bramer, X. Wu
Pages186-193
Number of pages8
DOIs
Publication statusPublished - 1 Dec 2004
EventProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 - Brighton, United Kingdom
Duration: 1 Nov 20044 Nov 2004

Publication series

NameProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Other

OtherProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
CountryUnited Kingdom
CityBrighton
Period1/11/044/11/04

    Fingerprint

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Sequeira, K., & Zaki, M. (2004). SCHISM: A new approach for interesting subspace mining. In R. Rastogi, K. Morik, M. Bramer, & X. Wu (Eds.), Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 (pp. 186-193). (Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004). https://doi.org/10.1109/ICDM.2004.10099