A framework for ontology-driven subspace clustering

Jinze Liu, Wei Wang, Jiong Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

24 Scopus citations

Abstract

Traditional clustering is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. While domain knowledge is always the best way to justify clustering, few clustering algorithms have ever take domain knowledge into consideration. In this paper, the domain knowledge is represented by hierarchical ontology. We develop a framework by directly incorporating domain knowledge into clustering process, yielding a set of clusters with strong ontology implication. During the clustering process, ontology information is utilized to efficiently prune the exponential search space of the subspace clustering algorithms. Meanwhile, the algorithm generates automatical interpretation of the clustering result by mapping the natural hierarchical organized subspace clusters with significant categorical enrichment onto the ontology hierarchy. Our experiments on a set of gene expression data using gene ontology demonstrate that our pruning technique driven by ontology significantly improve the clustering performance with minimal degradation of the cluster quality. Meanwhile, many hierarchical organizations of gene clusters corresponding to a sub-hierarchies in gene ontology were also successfully captured.

Original languageEnglish
Title of host publicationKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages623-628
Number of pages6
DOIs
StatePublished - 2004
EventKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Seattle, WA, United States
Duration: Aug 22 2004Aug 25 2004

Publication series

NameKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

ConferenceKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Country/TerritoryUnited States
CitySeattle, WA
Period8/22/048/25/04

Keywords

  • Ontology
  • Subspace clustering
  • Tendency Preserving

ASJC Scopus subject areas

  • General Engineering

Fingerprint

Dive into the research topics of 'A framework for ontology-driven subspace clustering'. Together they form a unique fingerprint.

Cite this