Clustering of SNP data with application to genomics

Kwok Po NG, Mark J. Li, Sio I. Ao, Pak C. Sham, Yiu Ming CHEUNG, Joshua Z. Huang

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Single nucleotide polymorphisms (SNPs) are very common throughout the genome and hence are potentially valuable for mapping disease susceptibility loci by detecting association between SNP markers and disease. Many methods may only be applicable when marker haplotypes, rather than genotypes (categorical data), are available for analysis. In this paper, we explore the properties of k-modes (categorical data) clustering algorithms to SNP data for detecting association between SNP markers and disease. Subspace k-modes clustering properties are also considered and tested.

Original languageEnglish
Title of host publicationProceedings - ICDM Workshops 2006 - 6th IEEE International Conference on Data Mining - Workshops
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages158-162
Number of pages5
ISBN (Print)0769527027, 9780769527024
DOIs
Publication statusPublished - 2006

Publication series

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

Scopus Subject Areas

  • Engineering(all)

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