On post-clustering evaluation and modification
Pattern Recognition LettersPublished 1 May 2000
Sim Heng Ong, Xingwang Zhao
Citations9
SJR quartileQ1
SJR score1.00
SNIP1.43
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TL;DR
A new approach is proposed in which the concept of cluster density is introduced to assess the quality of an algorithmically generated partition and accordingly guide an amelioration process through split-and-merge operations.
Abstract
Unsupervised clustering algorithms sometimes do not lead to meaningful interpretations of the structure in the data. We propose a new approach in which the concept of cluster density is introduced to assess the quality of an algorithmically generated partition and accordingly guide an amelioration process through split-and-merge operations.
Keywords
Computer Science
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