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Microarray data clustering based on temporal variation: FCV with TSD preclustering.

Möller-Levet, CS, Cho, KH and Wolkenhauer, O (2003) Microarray data clustering based on temporal variation: FCV with TSD preclustering. Appl Bioinformatics, 2 (1). pp. 35-45.

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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.

Item Type: Article
Divisions : Surrey research (other units)
Authors : Möller-Levet, CS, Cho, KH and Wolkenhauer, O
Date : 2003
Uncontrolled Keywords : Algorithms, Cluster Analysis, Computer Simulation, Fuzzy Logic, Gene Expression Profiling, Models, Genetic, Oligonucleotide Array Sequence Analysis, Pattern Recognition, Automated, Time Factors
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 09:52
Last Modified : 24 Jan 2020 17:53

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