A Modified K-means Algorithm - Two-Layer K-means Algorithm

被引：6
|
作者：
Liu, Chen-Chung [1 ]
Chu, Shao-Wei [2 ]
Chan, Yung-Kuan [3 ]
Yu, Shyr-Shen [2 ]
机构：
[1] Natl Chin Yi Univ Technol Taichung, Dept Elect Engn, Taichung, Taiwan
[2] Natl Chung Hsing Univ, Dept Comp Sci & Engn, Taichung 40227, Taiwan
[3] Natl Chung Hsing Univ, Dept Management Informat Syst, Taichung 40227, Taiwan
来源：
2014 TENTH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING (IIH-MSP 2014) | 2014年
关键词：
K-means algorithm; Classification; subcluster;
D O I：
10.1109/IIH-MSP.2014.118
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a modified K-means algorithm is proposed to categorize a set of data. K-means algorithm is a simple and easy clustering method which can efficiently classify a large number of continuous numerical data of high-dimensions. Moreover, the data in each cluster are similar to one another. However, it is vulnerable to outliers and noisy data, and it spends much executive time in classifying data too. Noisy data, outliers, and the data with quite different values in one cluster may reduce the accuracy rate of data matching obtained by a pattern matching system since the cluster center cannot precisely describe the data in the cluster. Hence, this study provides a two-layer K-means algorithm to solve above problems. In experiment, several well-known data sets are used to evaluate the performance of proposed algorithm, and the two-layer K-means algorithm can give expressive experimental results.
引用
收藏
页码：447 / 450
页数：4
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