The Improved Research on K-Means Clustering Algorithm in Initial Values

被引：0
|
作者：
Liu Guoli [1 ]
Li Yanping [2 ]
Wang Tingting [1 ]
Gao Jinqiao [1 ]
Yu Limei [1 ]
机构：
[1] HeBei Univ Technol, Langfang, Hebei, Peoples R China
[2] HeBei Univ Technol, Sch Comp Sci & Engn, Tianjin, Peoples R China
来源：
PROCEEDINGS 2013 INTERNATIONAL CONFERENCE ON MECHATRONIC SCIENCES, ELECTRIC ENGINEERING AND COMPUTER (MEC) | 2013年
关键词：
data mining; clustering analysis; k-means; initial values;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper deeply works over the aspect that the k-means c1usteriug algorithm is very seusitive to the iuitial values. In order to improve the dependence on the initial values, it proposes a new algorithm called K-means clustering algorithm based on iterative density (hereinafter referred to as IDKM). Through continuous modification to density threshold, it gets the more clustering centers, and merges them until the specified number of clustering center is met. IDKM algorithm is applied to the IRIS data set for clustering analysis, and then the result proves that the improved algorithm optimizes the dependence; Finally, IDKM is applied to Student achievement data set, the analysis of the clustering results guides students to study, it realizes the application of K-means clustering algorithm on data mining.
引用
收藏
页码：2124 / 2127
页数：4
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