Multiscale Time Series Clustering : A Consensus Clustering Approach

• Item type: SIG Technical Reports(1)
• 公開日: 2004-12-07
• タイトル: Multiscale Time Series Clustering : A Consensus Clustering Approach
• タイトル / 言語: en
• タイトル: Multiscale Time Series Clustering : A Consensus Clustering Approach
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: School of Knowledge Science Japan Advanced Institute of Science and Technology
• 著者所属: School of Knowledge Science Japan Advanced Institute of Science and Technology
• 著者所属(英): en
• 著者所属(英): School of Knowledge Science Japan Advanced Institute of Science and Technology
• 著者所属(英): en
• 著者所属(英): School of Knowledge Science Japan Advanced Institute of Science and Technology
• 著者名: Hui, Zhang; TuBaoHo
• 著者名(英): Hui, Zhang; Tu, BaoHo
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Multiscale analysis has played an important role in signal processing and statistics. Multiscale analysis analyzes the time series data in multiple scales and each scale catches specific features of the data thus it gives us the ability of observing time series data in various views. When applying multiscale analysis to time series clustering it is not trivial to determine the appropriate scale. This problem is circumvented in this paper by getting a consensus of the clustering solutions over each scale. We first perform clustering with the data of each scale then merge the cluster solutions together. Two contributions of this paper are: we propose a multiscale time series clustering scheme based on the consensus of multiple clustering and a novel consensus clustering algorithm is presented in this paper. The benefits of the proposed approach are experimental evaluated with several real time series data.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Multiscale analysis has played an important role in signal processing and statistics. Multiscale analysis analyzes the time series data in multiple scales, and each scale catches specific features of the data, thus it gives us the ability of observing time series data in various views. When applying multiscale analysis to time series clustering, it is not trivial to determine the appropriate scale. This problem is circumvented in this paper by getting a consensus of the clustering solutions over each scale. We first perform clustering with the data of each scale, then merge the cluster solutions together. Two contributions of this paper are: we propose a multiscale time series clustering scheme based on the consensus of multiple clustering and a novel consensus clustering algorithm is presented in this paper. The benefits of the proposed approach are experimental evaluated with several real time series data.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11135936
• 書誌情報: 情報処理学会研究報告知能と複雑系（ICS）; 巻 2004, 号 125(2004-ICS-138), p. 237-242, 発行日 2004-12-07
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会