DocumentCode :
37991
Title :
Noise Resistant Generalized Parametric Validity Index of Clustering for Gene Expression Data
Author :
Rui Fa ; Nandi, Asoke K.
Author_Institution :
Dept. of Electron. & Comput. Eng., Brunel Univ., Uxbridge, UK
Volume :
11
Issue :
4
fYear :
2014
fDate :
July-Aug. 2014
Firstpage :
741
Lastpage :
752
Abstract :
Validity indices have been investigated for decades. However, since there is no study of noise-resistance performance of these indices in the literature, there is no guideline for determining the best clustering in noisy data sets, especially microarray data sets. In this paper, we propose a generalized parametric validity (GPV) index which employs two tunable parameters α and β to control the proportions of objects being considered to calculate the dissimilarities. The greatest advantage of the proposed GPV index is its noise-resistance ability, which results from the flexibility of tuning the parameters. Several rules are set to guide the selection of parameter values. To illustrate the noise-resistance performance of the proposed index, we evaluate the GPV index for assessing five clustering algorithms in two gene expression data simulation models with different noise levels and compare the ability of determining the number of clusters with eight existing indices. We also test the GPV in three groups of real gene expression data sets. The experimental results suggest that the proposed GPV index has superior noise-resistance ability and provides fairly accurate judgements.
Keywords :
genetics; pattern clustering; GPV index; clustering algorithms; gene expression data clustering; gene expression data sets; gene expression data simulation models; microarray data sets; noise levels; noise resistant generalized parametric validity index; noise-resistance performance; tunable parameters; Bioinformatics; Clustering algorithms; Computational biology; Gene expression; Noise measurement; Clustering validity index; gene expression analysis; microarray; noise resistance;
fLanguage :
English
Journal_Title :
Computational Biology and Bioinformatics, IEEE/ACM Transactions on
Publisher :
ieee
ISSN :
1545-5963
Type :
jour
DOI :
10.1109/TCBB.2014.2312006
Filename :
6774444
Link To Document :
بازگشت