An efficient line symmetry-based K-means algorithm
Pattern Recognition LettersPublished 11 January 2006
Kuo‐Liang Chung, Keng-Sheng Lin
Citations17
SJR quartileQ1
SJR score1.00
SNIP1.43
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TL;DR
This paper presents a novel line symmetry-based K-means algorithm for clustering the data set with line symmetry property and based on some real data sets, experimental results demonstrate that this algorithm is rather encouraging.
Abstract
Recently, Su and Chou presented an efficient point symmetry-based K-means algorithm. Extending their point symmetry-based K-means algorithm, this paper presents a novel line symmetry-based K-means algorithm for clustering the data set with line symmetry property. Based on some real data sets, experimental results demonstrate that our proposed line symmetry-based K-means algorithm is rather encouraging.
Keywords
Computer Science
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