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Data clustering using hierarchical deterministic annealing and higher order statistics

DSpace at IIT Bombay

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Field Value
 
Title Data clustering using hierarchical deterministic annealing and higher order statistics
 
Creator DESAI, UB
RAJAGOPALAN, AN
JAIN, AVINASH
 
Subject genetic algorithms
perturbation techniques
signal distortion
simulated annealing
 
Description In 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.
 
Publisher IEEE
 
Date 2008-11-21T06:46:59Z
2011-11-25T12:30:15Z
2011-12-26T13:04:53Z
2011-12-27T05:50:55Z
2008-11-21T06:46:59Z
2011-11-25T12:30:15Z
2011-12-26T13:04:53Z
2011-12-27T05:50:55Z
1999
 
Type Article
 
Identifier IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 46(8), 1100-104
1057-7130
http://dx.doi.org/10.1109/82.782060
http://hdl.handle.net/10054/75
http://dspace.library.iitb.ac.in/xmlui/handle/10054/75
 
Language en_US