Stable Clustering of Temporal Gene Expression Data

K-means and its many variants are widely used in bioinformatics. However, one of the drawbacks is that the clustering results are dependent of the initial choice of centroids, hence the experimental results are less reproducible. In addition, poor handling of nonspherical clusters is another weakness of the K-means algorithm. Thus, we investigate spectral clustering on temporal gene expression data in this paper. Experimental results show that when combined with appropriate mother wavelet, spectral clustering is able to improve both clustering accuracy and stability for temporal data, as compared with the K-means