A NEAR-OPTIMAL INITIAL SEED VALUE SELECTION IN K-MEANS ALGORITHM USING A GENETIC ALGORITHM

被引：95

作者：

BABU, GP ^{[1
]}

MURTY, MN ^{[1
]}

机构：

[1] INDIAN INST SCI,DEPT COMP SCI & AUTOMAT,BANGALORE 560012,KARNATAKA,INDIA

来源：

PATTERN RECOGNITION LETTERS | 1993年 / 14卷 / 10期

关键词：

CLUSTERING; SEED VALUES; OPTIMAL PARTITION; GENETIC ALGORITHMS; K-MEANS ALGORITHM;

D O I：

10.1016/0167-8655(93)90058-L

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The K-means algorithm for clustering is very much dependent on the initial seed values. We use a genetic al to find a near-optimal partitioning of the given data set by selecting proper initial seed values in the K-means algorithm. Results obtained are very encouraging and in most of the cases, on data sets having well separated clusters, the proposed scheme reached a global minimum.

引用

页码：763 / 769

页数：7

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