Semi-supervised graph clustering: a kernel approach

Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semisupervised clustering algorithms are designed for data represented as vectors. In this paper, we unify vector-based and graph-based approaches. We show that a recently-proposed objective function for semi-supervised clustering based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of constraint penalty functions, can be expressed as a special case of the weighted kernel k-means objective. A recent theoretical connection between kernel kmeans and several graph clustering objectives enables us to perform semi-supervised clustering of data given either as vectors or as a graph. For vector data, the kernel approach also enables us to find clusters with nonlinear boundaries in the input data space. Furthermore, we show that recent work on spectra...

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney
• Keywords: Graph Clustering Objectives, ICML 2005, Machine Learning, Semi-supervised Clustering Algorithms, State-of-the-art Semi-supervised Algorithms