Gene clusters could be derived based on expression profiles, function categorization and promoter regions. To obtain thorough understanding of gene expression and regulation, the three aspects should be combined in an organic way. In this study, we explored the possible ways to analyze the large-scale gene expression data. Three approaches were used to analyze yeast temporal expression data: 1) start from clustering on the expression profiles followed by function categorization and promoter analysis, 2) start from function categorization followed by clustering on expression profiles and promoter analysis, and 3) start from clustering on the promoter region followed by clustering on expression profiles. For clustering analysis on the time-series data, we developed a largest-first algorithm, which provide a mechanism for quality control on clusters. For promoter analysis, we developed a core-extension algorithm.