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dc.contributor.authorLi, C
dc.contributor.authorBiswas, G
dc.contributor.authorDale, M
dc.contributor.authorDale, P
dc.date.accessioned2017-05-03T11:11:53Z
dc.date.available2017-05-03T11:11:53Z
dc.date.issued2002
dc.identifier.issn1088-467X
dc.identifier.urihttp://hdl.handle.net/10072/6718
dc.description.abstractThis paper discusses a temporal data clustering system that is based on the Hidden Markov Model(HMM) methodology. The proposed methodology improves upon existing HMM clustering methods in two ways. First, an explicit HMM model size selection procedure is incorporated into the clustering process, i.e., the sizes of the individual HMMs are dynamically determined for each cluster. This improves the interpretability of cluster models, and the quality of the final clustering partition results. Second, a partition selection method is developed to ensure an objective, data-driven selection of the number of clusters in the partition. The result is a heuristic sequential search control algorithm that is computationally feasible. Experiments with artificially generated data and real world ecology data show that: (i) the HMM model size selection algorithm is effective in re-discovering the structure of the generating HMMs, (ii) the HMM clustering with model size selection significantly outperforms HMM clustering using uniform HMM model sizes for re-discovering clustering partition structures, (iii) it is able to produce interpretable and "interesting" models for real world data.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoen_US
dc.publisherIOS Press
dc.publisher.placeNetherlands
dc.publisher.urihttp://content.iospress.com/articles/intelligent-data-analysis/ida00093
dc.relation.ispartofpagefrom281
dc.relation.ispartofpageto308
dc.relation.ispartofedition2002
dc.relation.ispartofissue3
dc.relation.ispartofjournalIntelligent Data Analysis
dc.relation.ispartofvolume6
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchData Format
dc.subject.fieldofresearchCognitive Sciences
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0804
dc.subject.fieldofresearchcode1702
dc.title"Matryoshka: A HMM Based Temporal Data Clustering Methodology for Modeling System Dynamics"
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2015-05-11T05:37:52Z
gro.hasfulltextNo Full Text
gro.griffith.authorDale, Patricia E.
gro.griffith.authorDale, Michael B.


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