Microarray data clustering based on temporal variation: FCV with TSD preclustering.
The aim of this paper is to present a new clustering algorithm for short time-series gene expression data that is able to characterise temporal relations in the clustering environment (ie data-space), which is not achieved by other conventional clustering algorithms such as k -means or hierarchical clustering. The algorithm called fuzzy c -varieties clustering with transitional state discrimination preclustering (FCV-TSD) is a two-step approach which identifies groups of points ordered in a line configuration in particular locations and orientations of the data-space that correspond to similar expressions in the time domain. We present the validation of the algorithm with both artificial and real experimental datasets, where k -means and random clustering are used for comparison. The performance was evaluated with a measure for internal cluster correlation and the geometrical properties of the clusters, showing that the FCV-TSD algorithm had better performance than the k -means algorithm on both datasets.
Möller-Levet
Carla S
CS
Department of Electrical Engineering and Electronics, Control Systems Centre, University of Manchester Institute of Science and Technology (UMIST), Manchester, UK.
Cho
Kwang-Hyun
KH
Wolkenhauer
Olaf
O
D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013997:Time Factors