Remotely Sensed Data Clustering Using K-Harmonic Means Algorithm and Cluster Validity Index

被引：6
|
作者：
Mahi, Habib [1 ]
Farhi, Nezha [1 ]
Labed, Kaouter [2 ]
机构：
[1] Ctr Space Tech, Earth Observat Div, Arzew, Algeria
[2] Univ USTOMB, Fac Math & Comp Sci Mohamed Boudiaf, Oran, Algeria
来源：
COMPUTER SCIENCE AND ITS APPLICATIONS, CIIA 2015 | 2015年 / 456卷
关键词：
Clustering; KHM; Cluster validity indices; Remotely sensed data; K-means; FCM;
D O I：
10.1007/978-3-319-19578-0_9
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we propose a new clustering method based on the combination of K-harmonic means (KHM) clustering algorithm and cluster validity index for remotely sensed data clustering. The KHM is essentially insensitive to the initialization of the centers. In addition, cluster validity index is introduced to determine the optimal number of clusters in the data studied. Four cluster validity indices were compared in this work namely, DB index, XB index, PBMF index, WB-index and a new index has been deduced namely, WXI. The Experimental results and comparison with both K-means (KM) and fuzzy C-means (FCM) algorithms confirm the effectiveness of the proposed methodology.
引用
收藏
页码：105 / 116
页数：12
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