Murty, Narasimha M and Krishna, G (1980) A computationally efficient technique for data-clustering. In: Pattern Recognition, 12 (3). pp. 153-158.
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A computationally efficient agglomerative clustering algorithm based on multilevel theory is presented. Here, the data set is divided randomly into a number of partitions. The samples of each such partition are clustered separately using hierarchical agglomerative clustering algorithm to form sub-clusters. These are merged at higher levels to get the final classification. This algorithm leads to the same classification as that of hierarchical agglomerative clustering algorithm when the clusters are well separated. The advantages of this algorithm are short run time and small storage requirement. It is observed that the savings, in storage space and computation time, increase nonlinearly with the sample size.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Elsevier Science.|
|Keywords:||Nonparametric;Agglomerative;Multilevel theory; Partitioning; Relabeling;Representative samples;Well separated clusters.|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||23 Jul 2009 05:50|
|Last Modified:||19 Sep 2010 05:38|
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