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dc.contributor.authorDESAI, UBen_US
dc.contributor.authorRAJAGOPALAN, ANen_US
dc.contributor.authorJAIN, AVINASHen_US
dc.date.accessioned2008-11-21T06:46:59Zen_US
dc.date.accessioned2011-11-25T12:30:15Zen_US
dc.date.accessioned2011-12-26T13:04:53Zen_US
dc.date.accessioned2011-12-27T05:50:55Z
dc.date.available2008-11-21T06:46:59Zen_US
dc.date.available2011-11-25T12:30:15Zen_US
dc.date.available2011-12-26T13:04:53Zen_US
dc.date.available2011-12-27T05:50:55Z
dc.date.issued1999en_US
dc.identifier.citationIEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 46(8), 1100-104en_US
dc.identifier.issn1057-7130en_US
dc.identifier.urihttp://dx.doi.org/10.1109/82.782060en_US
dc.identifier.urihttp://hdl.handle.net/10054/75en_US
dc.identifier.urihttp://dspace.library.iitb.ac.in/xmlui/handle/10054/75en_US
dc.description.abstractIn this brief, we propose an extension to the hierarchical deterministic annealing (HDA) algorithm for clustering by incorporating additional features into the algorithm. To decide a split in a cluster, the interdependency among all the clusters is taken into account by using the entire data distribution. A general distortion measure derived from the higher order statistics (HOS) of the data is used to analyze the phase transitions. Experimental results clearly demonstrate the improvement in the performance of the HDA algorithm when the interdependency among the clusters and the HOS of the data points are also utilized for the purpose of clustering.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectPerturbation Techniquesen_US
dc.subjectSignal Distortionen_US
dc.subjectSimulated Annealingen_US
dc.titleData clustering using hierarchical deterministic annealing and higher order statisticsen_US
dc.typeArticleen_US
dc.description.copyrightIEEEen_US


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