Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering short time series gene expression data
Bioinformatics
A tutorial on spectral clustering
Statistics and Computing
Novel Algorithm for Coexpression Detection in Time-Varying Microarray Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Clustering Time-Series Gene Expression Data with Unequal Time Intervals
Transactions on Computational Systems Biology X
Microarray Time-Series Data Clustering via Multiple Alignment of Gene Expression Profiles
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Clustering of unevenly sampled gene expression time-series data
Fuzzy Sets and Systems
Clustering microarray time-series data using expectation maximization and multiple profile alignment
BIBMW '09 Proceedings of the 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop
Clustering gene expression series with prior knowledge
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
A new fuzzy cover approach to clustering
IEEE Transactions on Fuzzy Systems
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It has been recently shown that traditional clustering methods do not necessarily perform well on time-series data, because of the temporal relationships involved in such data. This makes it a particularly difficult problem. In this paper, we propose to use spectral clustering approaches for clustering microarray time-series data. The approaches are based on two transformations that have been recently introduced, especially for gene expression time-series data, namely, alignment-based and variation-based transformations. Both transformations have been devised in order to take into account temporal relationships in the data, and have been shown to increase the ability of a clustering method in detecting co-expressed genes. We investigate the performances of these transformations methods, when combined with spectral clustering on two microarray time-series datasets, and discuss their strengths and weaknesses. Our experiments on two well known real-life datasets show the superiority of the alignment-based over the variation-based transformation for finding meaningful groups of co-expressed genes.