Hypergraph based geometric biclustering algorithm
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 1656-1665 |
Journal / Publication | Pattern Recognition Letters |
Volume | 33 |
Issue number | 12 |
Publication status | Published - 1 Sept 2012 |
Link(s)
Abstract
In this paper, we present a hypergraph based geometric biclustering (HGBC) algorithm. In a high dimensional space, bicluster patterns to be recognized can be considered to be linear geometrical structures. We can use the Hough transform (HT) to find sub-biclusters which correspond to the linear structures in column-pair spaces. Then a hypergraph model is built to merge the sub-biclusters into larger ones. Experiments on simulated and real biological data show that the HGBC algorithm proposed here can combine the sub-biclusters efficiently and provide more accurate classification results compared with existing biclustering methods. © 2012 Elsevier B.V. All rights reserved.
Research Area(s)
- Biclustering, DNA microarray data analysis, Hough transform, Hypergraph partition
Citation Format(s)
Hypergraph based geometric biclustering algorithm. / Wang, Zhiguan; Yu, Chi Wai; Cheung, Ray C.C. et al.
In: Pattern Recognition Letters, Vol. 33, No. 12, 01.09.2012, p. 1656-1665.
In: Pattern Recognition Letters, Vol. 33, No. 12, 01.09.2012, p. 1656-1665.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review