About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
International Conference on Natural Computation 2007
Conference paper
Online detecting and tracking of the evolution of user communities
Abstract
Clustering, known as divide a set of static data points into densely distributed groups, has long been a well-known research area. However, many real-life problems require a novel and generalized form of clustering, the evolutionary clustering. Given a dynamic set of data points that may move, disappear and emerge, the evolutionary clustering is to track the move, disappear and emerge of the corresponding clusters. In this paper, we propose converting this novel problem into an iterative form of learning a mixture model, and present a structural-EM algorithm as the solution. © 2007 IEEE.