%0 Conference Proceedings
%T Remotely Sensed Data Clustering Using K-Harmonic Means Algorithm and Cluster Validity Index
%+ Centre National des Techniques Spatiales
%+ Université des sciences et de la Technologie d'Oran Mohamed Boudiaf [Oran] (USTO MB)
%A Mahi, Habib
%A Farhi, Nezha
%A Labed, Kaouter
%Z Part 4: Computational Intelligence: Machine Learning
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 5th International Conference on Computer Science and Its Applications (CIIA)
%C Saida, Algeria
%Y Abdelmalek Amine
%Y Ladjel Bellatreche
%Y Zakaria Elberrichi
%Y Erich J. Neuhold
%Y Robert Wrembel
%I Springer International Publishing
%3 Computer Science and Its Applications
%V AICT-456
%P 105-116
%8 2015-05-20
%D 2015
%R 10.1007/978-3-319-19578-0_9
%K Clustering
%K KHM
%K Cluster validity indices
%K Remotely sensed data
%K K-means
%K FCM
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 5
%2 https://inria.hal.science/hal-01789935/document
%2 https://inria.hal.science/hal-01789935/file/339159_1_En_9_Chapter.pdf
%L hal-01789935
%U https://inria.hal.science/hal-01789935
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-AICT-456
%~ IFIP-CIIA