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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhi-Hua Fan Da-Ke Huang Juan-Zi Li Ke-Hong Wang |
| Copyright Year | 2004 |
| Description | Author affiliation: Dept. of Comput. Sci. & Tech., Tsinghua Univ., Beijing, China (Zhi-Hua Fan; Da-Ke Huang; Juan-Zi Li; Ke-Hong Wang) |
| Abstract | A new time series clustering algorithm named morphing cluster dynamics is put forward. This algorithm can capture two levels of inter-time-series interaction behavior among a large set of time series: (i) group level, which includes the merges and splits of clusters, and (ii) individual-to-group level, such as a time series joins or leaves a cluster. In order to do so, it first reduces the TS set into a system morphing graph, a multipartite graph is employed to represent the latent interactions, and then extract strong interaction patterns from this graph. A system morphing graph is built by three steps: firstly, the input time series set is divided into sets of segments along the time line, and each segment set serves as a partite of the graph; then, each segment set is clustered and the resulting clusters serves as the vertices in the graph; third, the edges are built according to member-sharing relationships between clusters. System morphing graphs can model the captured TS interactions. Because this algorithm both cuts and concatenates time series, it does not fit into either whole clustering or subsequence clustering. |
| Starting Page | 1749 |
| Ending Page | 1754 |
| File Size | 494892 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780384032 |
| DOI | 10.1109/ICMLC.2004.1382058 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-08-26 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Clustering algorithms Hidden Markov models Clustering methods Partitioning algorithms Bayesian methods Algorithm design and analysis Machine learning Cybernetics Machine learning algorithms |
| Content Type | Text |
| Resource Type | Article |
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