A fast kernel-based multilevel algorithm for graph clustering

Graph clustering (also called graph partitioning) -- clustering the nodes of a graph -- is an important problem in diverse data mining applications. Traditional approaches involve optimization of graph clustering objectives such as normalized cut or ratio association; spectral methods are widely used for these objectives, but they require eigenvector computation which can be slow. Recently, graph clustering with a general cut objective has been shown to be mathematically equivalent to an appropriate weighted kernel k-means objective function. In this paper, we exploit this equivalence to develop a very fast multilevel algorithm for graph clustering. Multilevel approaches involve coarsening, initial partitioning and refinement phases, all of which may be specialized to different graph clustering objectives. Unlike existing multilevel clustering approaches, such as METIS, our algorithm does not constrain the cluster sizes to be nearly equal. Our approach gives a theoretical guarantee th...

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
• Keywords: Data Mining, Graph Clustering Objectives, KDD 2005, Multilevel Clustering Approaches, Spectral Clustering Algorithm