Integrated KL (K-means - Laplacian) Clustering: A New Clustering Approach by Combining Attribute Data and Pairwise Relations

Most datasets in real applications come in from multiple sources. As a result, we often have attributes information about data objects and various pairwise relations (similarity) between data objects. Traditional clustering algorithms use either data attributes only or pairwise similarity only. We propose to combine K-means clustering on data attributes and normalized cut spectral clustering on pairwise relations. We show that these two methods can be coherently integrated together to make use of different data sources to obtain good clustering results. We also show that our integrated KL (K-means Laplacian) clustering method can be naturally extended to semi-supervised clustering, data embedding and metric learning. Finally the experimental results on benchmark data sets are presented to show the effectiveness of our method.