Cluster analysis on time series gene expression data
Huang-Cheng Kuo, Tsung Lung Lee, Jen peng Huang · International Journal of Business Intelligence and Data Mining · 2009
Cluster analysis is frequently used to study the trend of gene expression behaviours from microarray time series data. We adopt a partitioning-based clustering algorithm for such a task. After time series are discritised into sequences, a sequential pattern mining technique is applied to find patterns as the initial clusters. Longest Common Subseries Similarity is used to measure the similarity between time series which overcomes the 'shift-effect' influence. An object is re-assigned to the cluster which has most objects within the k nearest neighbours of the object. Similarity measurements, like Pearson correlation coefficient, are used to determine the neighbours.