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

Appl Bioinformatics. 2003;2(1):35-45.


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.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms*
  • Cluster Analysis
  • Computer Simulation
  • Fuzzy Logic
  • Gene Expression Profiling / methods*
  • Models, Genetic*
  • Oligonucleotide Array Sequence Analysis / methods*
  • Pattern Recognition, Automated / methods*
  • Time Factors