Abstract：
:Microarray technology has been widely applied in study of measuring gene expression levels for thousands of genes simultaneously. Gene cluster analysis is found useful for discovering the function of gene because co-expressed genes are likely to share the same biological function. K-means is one of well-known clustering methods. However, it is sensitive to the selection of an initial clustering and easily becoming trapped in a local minimum. Particle-pair optimizer (PPO) is a variation on the traditional particle swarm optimization (PSO) algorithm, which is stochastic particle-pair based optimization technique that can be applied to a wide range of problems. In this paper we bridges PPO and K-means within the algorithm PK-means for the first time. Our results indicate that PK-means clustering is generally more accurate than K-means and Fuzzy K-means (FKM). PK-means also has better robustness for it is less sensitive to the initial randomly selected cluster centroids. Finally, our algorithm outperforms these methods with fast convergence rate and low computation load.
journal_name
Comput Biol Chemjournal_title
Computational biology and chemistryauthors
Du Z,Wang Y,Ji Zdoi
10.1016/j.compbiolchem.2008.03.020subject
Has Abstractpub_date
2008-08-01 00:00:00pages
243-7issue
4eissn
1476-9271issn
1476-928Xpii
S1476-9271(08)00031-5journal_volume
32pub_type
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