Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Topic Detection and Tracking: Event-Based Information Organization
Topic Detection and Tracking: Event-Based Information Organization
Event threading within news topics
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
NewsInEssence: summarizing online news topics
Communications of the ACM - The digital society
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A Novelty-based Clustering Method for On-line Documents
World Wide Web
IEEE Transactions on Knowledge and Data Engineering
T-Scroll: visualizing trends in a time-series of documents for interactive user exploration
ECDL'07 Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries
Hi-index | 0.00 |
Document clustering methods for time-series documents produce a sequence of snapshots of clustering results over time. Analyzing the contents (topics) and trends in a long sequence of clustering snapshots is hard and requires efforts since there are too many number of clusters; a user may need to access every cluster or read every document contained in each cluster. In this paper, we propose a framework to find clusters of user interest and change patterns called transition patterns involving the clusters. A cluster in a clustering result may persist in 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.