Discovering correlated subspace clusters in 3D continuous-valued data

Kelvin Sim, Zeyar Aung, Vivekanand Gopalkrishnan

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

28 Scopus citations

Abstract

Subspace clusters represent useful information in high-dimensional data. However, mining significant subspace clusters in continuous-valued 3D data such as stock-financial ratio-year data, or gene-sample-time data, is difficult. Firstly, typical metrics either find subspaces with very few objects, or they find too many insignificant subspaces - those which exist by chance. Besides, typical 3D subspace clustering approaches abound with parameters, which are usually set under biased assumptions, making the mining process a 'guessing game'. We address these concerns by proposing an information theoretic measure, which allows us to identify 3D subspace clusters that stand out from the data. We also develop a highly effective, efficient and parameter-robust algorithm, which is a hybrid of information theoretical and statistical techniques, to mine these clusters. From extensive experimentations, we show that our approach can discover significant 3D subspace clusters embedded in 110 synthetic datasets of varying conditions. We also perform a case study on real-world stock datasets, which shows that our clusters can generate higher profits compared to those mined by other approaches.

Original languageBritish English
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining, ICDM 2010
Pages471-480
Number of pages10
DOIs
StatePublished - 2010
Event10th IEEE International Conference on Data Mining, ICDM 2010 - Sydney, NSW, Australia
Duration: 14 Dec 201017 Dec 2010

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference10th IEEE International Conference on Data Mining, ICDM 2010
Country/TerritoryAustralia
CitySydney, NSW
Period14/12/1017/12/10

Keywords

  • 3D subspace clustering
  • Financial data mining
  • Information theory

Fingerprint

Dive into the research topics of 'Discovering correlated subspace clusters in 3D continuous-valued data'. Together they form a unique fingerprint.

Cite this