FICA: A new data clustering technique based on partitional approach for data mining

被引：0

作者：

Tsai, Cheng-Fa ^{[1
]}

Shih, Deng-Chiung ^{[1
]}

Liu, Chih-Wei ^{[1
]}

机构：

[1] Natl Pingtung Univ Sci & Technol, Dept Management Informat Syst, Pingtung, Taiwan

来源：

PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2007年

关键词：

data miming; nearest neighbor; data clustering;

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：

This paper adopts the idea of nearest neighbor and proposes a new approach called Fast Intuitive Clustering Approach (FICA). Besides, FICA also adds the concept of data compression to lower the operating times and coordinates with parameters to reach global search. A series of experiments have been conducted on FICA and other clustering algorithms, like K-Means and DBSCAN. According to the simulation results, it is observed that the proposed FICA clustering algorithm outperforms K-Means and DBSCAN. FICA can not only to perform good efficiency and correctness but also be applied in large number of data sets. Finally, the proposed FICA is applied in face recognition problem.

引用

页码：739 / 744

页数：6

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