Querying Topic Evolution in Time Series Document Clusters

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概要

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• A document clusteringmethod 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 there are 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.

• Information and Media Technologies 編集運営会議の論文

著者

• Khy Sophoin

  Graduate School Of Systems And Information Engineering University Of Tsukuba

• Ishikawa Yoshiharu

  Information Technology Center Nagoya University

• Kitagawa Hiroyuki

  Graduate School Of Environmental Studies Nagoya University

• KITAGAWA Hiroyuki

  Graduate School of System and Information Engineering / Center for Computational Science, University of Tsukuba

• KHY Sophoin

  Graduate School of System and Information Engineering, University of Tsukuba

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• Interactive Outlier Detection Adaptive to Users' Intentions (夏のデータベースワークショップ(DBWS2004))
• Efficient Continual Top-k Keyword Search in Relational Databases
• Example-Based Outlier Detection for High Dimensional Datasets
• Querying Topic Evolution in Time Series Document Clusters
• Example-Based Outlier Detection for High Dimensional Datasets

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