A Spectral Co-Clustering Approach for Dynamic Data

A common task in many domains with a temporal aspect involves identifying and tracking clusters over time. Often dynamic data will have a feature-based representation. In some cases, a direct mapping will exist for both objects and features over time. But in many scenarios, smaller subsets of objects or features alone will persist across successive time periods. To address this issue, we propose a dynamic spectral co-clustering algorithm for simultaneously clustering objects and features over time, as represented by a set of related bipartite graphs. We evaluate the algorithm on several synthetic datasets, a benchmark text corpus, and social bookmarking data.