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.

Full text not available from this repository.

Abstract

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 :	Author
Depositing User :	Symplectic Elements
Date Deposited :	17 May 2017 09:52
Last Modified :	24 Jan 2020 17:53
URI:	http://epubs.surrey.ac.uk/id/eprint/825459

Actions (login required)

View Item

Downloads