Co-Clustering-Based Clustering and Segmentation for Pattern Discovery from Time Course Data
Time course data may inherit critical temporal ordering in contiguous (i.e. neighboring) time slot. Traditional one-way K-means clustering algorithms handle time points independently, ignoring the internal time locality. Although, co-clustering algorithms can discover latent local patterns, the discovered patterns are not necessary to be in a continuous time order. Therefore, this paper targets to extend an existing co-clustering framework to be applicable to time course data so that time-dependent local segment patterns over specific intervals can be captured.