title: Integration of clustering and biclustering procedures for analysis of large DNA microarray datasets.
abstract: In this paper, a new biclustering method is introduced based on a geometric method. The Hough Transform (HT) in column-pair space is used to find sub-biclusters and the k-means method is used to optimise the combining process. The experiment results show that the K-means Based Geometric Biclustering (KGBC) algorithm proposed here reduces the computing time for combining sub-biclusters and provides considerably higher classification accuracy compared with existing methods. Experiments on both simulated and real microarray data demonstrate that our method can identify biclusters with different noise levels and overlapping degrees.
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong. zhiguwang2@student.cityu.edu.hk
authors.forename: Doris Z
authors.initials: DZ
authors.lastname: Wang
authors.forename: Hong
authors.initials: H
authors.lastname: Yan
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D004271:DNA, Fungal / Q000235:genetics; D003627:Data Interpretation, Statistical; D030561:Databases, Nucleic Acid / Q000706:statistics & numerical data*; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D012441:Saccharomyces cerevisiae / Q000235:genetics; D049490:Systems Biology / Q000706:statistics & numerical data
chemical_list: D004271:DNA, Fungal