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
收藏
页码：250 / 253
页数：4
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