A Modified K-means Algorithms - Bi-Level K-Means Algorithm

被引：0
|
作者：
Yu, Shyr-Shen [1 ]
Chu, Shao-Wei [1 ]
Wang, Ching-Lin [2 ]
Chan, Yung-Kuan [3 ]
Chuang, Chia-Yi [1 ]
机构：
[1] Natl Chung Hsing Univ, Dept Comp Sci & Engn, Hsinchu, Taiwan
[2] Natl Chin Yi Univ Technol, Dept Informat Mgt, Taichung, Taiwan
[3] Natl Chung Hsing Univ, Dept Management Informat Syst, Taichung, Taiwan
来源：
PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON SOFT COMPUTING IN INFORMATION COMMUNICATION TECHNOLOGY | 2014年
关键词：
Clustering; K-means algorithm; Classification; Genetic algorithm;
D O I：
暂无
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 into smaller clusters. K-means algorithm is a simple and easy clustering method which can efficiently separate a huge number of continuous numerical data with 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 partitioning data too. Noisy data, outliers, and the data with quite different values in one cluster may reduce the accuracy rate of data clustering since the cluster center cannot precisely describe the data in the cluster. In this paper, a bi-level K-means algorithm is hence provided to solve the problems mentioned above. The bi-level K-means algorithm can give an expressive experimental results.
引用
收藏
页码：10 / 13
页数：4
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