An Effective Clustering Algorithm for Data Mining

被引：2

作者：

Vijendra, Singh ^{[1
]}

Ashwini, Kelkar ^{[1
]}

Laxman, Sahoo ^{[2
]}

机构：

[1] Mody Inst Technol & Sci, Fac Engn & Technol, Sikar, Rajasthan, India

[2] Northern India Engn Coll, Dept Comp Sci & Engn, Lucknow, UP, India

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA STORAGE AND DATA ENGINEERING (DSDE 2010) | 2010年

关键词：

Clustering; K-means; Genetic algorithm; K-MEANS ALGORITHM;

D O I：

10.1109/DSDE.2010.34

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes an effective clustering algorithm for databases, which are benchmark data sets of data mining applications. We present a Genetic Clustering Algorithm (GCA) that finds a globally optimal partition of a given data sets into a specified number of clusters. The algorithm is distance-based and creates centroids. To evaluate the proposed algorithm, we use some artificial data sets and compare with results of K-means. Experimental results show that the proposed algorithm has better performance and efficiently finds accurate clusters.

引用

页码：250 / 253

页数：4

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