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Extensive studies have shown that analyzing microarray time series data is important in bioinformatics research and biomedical applications. An observation in the analysis of gene expression data is that many genes have similarity in their expression patterns and therefore appear to be co-regulated. Previously, the time series gene expression data was analyzed mainly by checking the global similarities between the gene expression profiles and local similarities were overlooked. Local similarities can provide useful insight into gene behavior. In this paper, we propose a clustering algorithm for analyzing the time series gene expression data to identify the gene clusters based on the phase-wise local similarities in the cell cycle. Our approach exploits the fact that the genes which are involved in one phase of a cell cycle would have a characteristic profile for time points belonging to that phase and may not be involved in other phases. Moreover, a gene that is clustered with a set of genes in one phase might be involved with a different set of genes in other phases. In the proposed approach, we first clustered the genes at every time point of a phase and group genes with similar expression profiles, i.e., we group those genes together which remain in the same cluster at every time point within a phase. The functions of genes were obtained from Gene Ontology. In this paper, the results are presented for different phases of a cell cycle. Candidate genes are identified for these phases and their groups are analyzed. We found that the group of candidate genes had few genes which are known to be involved. Furthermore, some genes are found to be involved in more than one phase with different set of genes. Results presented show that local similarities can provide useful insight into gene behavior. Results are compared with an existing algorithm, STEM. We have used a saccharomyces cerevisiae cell cycle microarray database which is part of the Stanford Microarray Databas- (SMD).