Interval-Valued Centroids in K-Means Algorithms

被引：4

作者：

Nordin, Benjamine ^{[1
]}

Hu, Chenyi ^{[1
]}

Chen, Bernard ^{[1
]}

Sheng, Victor S. ^{[1
]}

机构：

[1] Univ Cent Arkansas, Dept Comp Sci, Conway, AR 72035 USA

来源：

2012 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2012), VOL 1 | 2012年

基金：

美国国家科学基金会;

关键词：

D O I：

10.1109/ICMLA.2012.87

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The K-Means algorithms are fundamental in machine learning and data mining. In this study, we investigate interval-valued rather than commonly used point-valued centroids in the K-Means algorithm. Using a proposed interval peak method to select initial interval centroids, we have obtained overall quality improvement of clusters on a set of test problems in the Fundamental Clustering Problem Suite (FCPS).

引用

页码：478 / 481

页数：4

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