On mining micro-array data by Order-Preserving Submatrix.
We study the problem of pattern-based subspace clustering which is clustering by pattern similarity finds objects that exhibit a coherent pattern of rises and falls in subspaces. Applications of pattern-based subspace clustering include DNA micro-array data analysis. Our goal is to devise pattern-based clustering methods that are capable of: discovering useful patterns of various shapes, and discovering all significant patterns. Our approach is to extend the idea of Order-Preserving Submatrix (OPSM). We devise a novel algorithm for mining OPSM, show that OPSM can be generalised to cover most existing pattern-based clustering models and propose a number of extensions to the original OPSM model.
Department of Computer Science, University of Hong Kong, Hong Kong. lcheung@cs.hku.hk
Lin
L
Cheung
David W
DW
Cheung
Ben
B
Kao
Kevin Y
KY
Yip
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000737:chemistry; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D007090:Image Interpretation, Computer-Assisted; D016247:Information Storage and Retrieval; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated
D004247:DNA