Automatic generation of overview timelines
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Topic Detection and Tracking: Event-Based Information Organization
Topic Detection and Tracking: Event-Based Information Organization
ThemeRiver: Visualizing Thematic Changes in Large Document Collections
IEEE Transactions on Visualization and Computer Graphics
An On-Line Document Clustering Method Based on Forgetting Factors
ECDL '01 Proceedings of the 5th European Conference on Research and Advanced Technology for Digital Libraries
Proceedings of the 35th conference on Winter simulation: driving innovation
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
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
Novelty-based Incremental Document Clustering for On-line Documents
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
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
A Query Language and Its Processing for Time-Series Document Clusters
ICADL 08 Proceedings of the 11th International Conference on Asian Digital Libraries: Universal and Ubiquitous Access to Information
Visualization of text streams: a survey
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Early detection of buzzwords based on large-scale time-series analysis of blog entries
Proceedings of the 23rd ACM conference on Hypertext and social media
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On the Internet, a large number of documents such as news articles and online journals are delivered everyday. We often have to review major topics and topic transitions from a large time-series of documents, but it requires much time and effort to browse and analyze the target documents. We have therefore developed an information visualization system called T-Scroll (Trend/Topic-Scroll) to visualize the transition of topics extracted from those documents. The system takes periodical outputs of the underlying clustering system for a time-series of documents then visualizes the relationships between clusters as a scroll. Using its interaction facility, users can grasp the topic transitions and the details of topics for the target time period. This paper describes the idea, the functions, the implementation, and the evaluation of the T-Scroll system.