Assessment of Microarray Data Clustering Results Based on a New Geometrical Index for Cluster Validity

A measurement of cluster quality is often needed for DNA microarray data analysis. In this paper, we introduce a new cluster validity index, which measures geometrical features of the data. The essential concept of this index is to evaluate the ratio between the squared total length of the data eige...

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Vydáno v:Soft computing (Berlin, Germany) Ročník 11; číslo 4; s. 341 - 348
Hlavní autoři: Lam, Benson S. Y., Yan, Hong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Heidelberg Springer Nature B.V 01.02.2007
Témata:
Clustering
Data analysis
Datasets
DNA chips
ISSN:1432-7643, 1433-7479
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Shrnutí:A measurement of cluster quality is often needed for DNA microarray data analysis. In this paper, we introduce a new cluster validity index, which measures geometrical features of the data. The essential concept of this index is to evaluate the ratio between the squared total length of the data eigen-axes with respect to the between-cluster separation. We show that this cluster validity index works well for data that contain clusters closely distributed or with different sizes. We verify the method using three simulated data sets, two real world data sets and two microarray data sets. The experiment results show that the proposed index is superior to five other cluster validity indices, including partition coefficients (PC), General silhouette index (GS), Dunn’s index (DI), CH Index and I-Index. Also, we have given a theorem to show for what situations the proposed index works well.
Bibliografie:ObjectType-Article-1
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-006-0087-1