Querying Topic Evolution in Time Series Document Clusters

Abstract

A document clustering method for time series documents produces a sequence of clustering results over time. Analyzing the contents and trends in a long sequence of clustering results is a hard and tedious task since ther ea re too many number of clusters. In this paper, we propose a framework to find clusters of users’ topics of interest and evolution patterns called transition patterns involving the topics. A cluster in a clustering result may continue to appear in or move to another cluster, branch into more than one cluster, merge with other clusters to form one cluster, or disappear in the adjacent clustering result. This research aims at providing users facilities to retrieve specific transition patterns in the clustering results. For this purpose, we propose a query language for time series document clustering results and an approach to query processing. The first experimental results on TDT2 corpus clustering results are presented.