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

Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 456)

Abstract

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.

Keywords

Clustering KHM Cluster validity indices Remotely sensed data K-means FCM 
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Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  1. 1.Earth Observation DivisionCentre of Space TechniquesArzewAlgeria
  2. 2.Kaouter LABED, Faculty of Mathematics and Computer Science Mohamed BoudiafUniversity - USTOMBOranAlgeria

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