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.
Chapter PDF
Similar content being viewed by others
References
Gan, G., Ma, C., Wu, J.: Data Clustering: Theory, Algorithms, and Applications. ASA-SIAM Serieson Statistics and Applied Probability. SIAM, Philadelphia (2007)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood (1988)
Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: A Study of Some Fuzzy Cluster Validity Indices, Genetic Clustering and Application to Pixel Classification. Fuzzy Sets and Systems 155, 191–214 (2005)
Bezdeck, J.C.: FCM: Fuzzy C-Means algorithm. Computers and Geoscience 10, 191–203 (1984)
Gong, X.-J., Ci, L.-L., Yao, K.-Z.: A FCM algorithm for remote-sensing image classification considering spatial relationship and its parallel implementation. In: International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2007, November 2-4, vol. 3, pp. 994–998 (2007)
Gao, Y., Wang, S., Liu, S.: Automatic Clustering Based on GA-FCM for Pattern Recognition. In: Second International Symposium on Computational Intelligence and Design, ISCID 2009, December 12-14, vol. 2, pp. 146–149 (2009)
McQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proc. 5th Berkeley Symp. Mathematics, Statistics and Probability, pp. 281–296 (1967)
Ball, G., Hall, D.: ISODATA: A novel method of data analysis and pattern classification. In Technical report, Stanford Research Institute, Menlo Park, CA, USA (1965)
Huang, K.: A Synergistic Automatic Clustering Technique (Syneract) for Multispectral Image Analysis. Photogrammetric Engineering and Remote Sensing 1(1), 33–40 (2002)
Zhao, Q.: Cluster validity in clustering methods. Ph.D. dissertation. University of Eastern Finland (2012)
Korgaonkar, G.S., Sedamkar, R.R., KiranBhandari.: Hyperspectral Image Classification on Decision level fusion. In: IJCA Proceedings on International Conference and Workshop on Emerging Trends in Technology, vol. 7, pp. 1–9 (2012)
Xie, X.L., Beni, A.: Validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 3, 841–846 (1991)
Bezdek, J.C.: Cluster validity with fuzzy sets. J. Cybernet. 3, 58–73 (1974)
Bezdek, J.C.: Mathematical models for systematics and taxonomy. In: Eighth International Conference on Numerical Taxonomy, San Francisco, CA, pp. 143–165 (1975)
Davies, D., Bouldin, D.: A cluster separation measure. IEEE PAMI 1(2), 224–227 (1979)
Dunn, J.C.: A fuzzy relative of the isodata process and its use in detecting compact well separated clusters. J. Cybernet. 3, 32–57 (1973)
Calinski, R.B., Harabasz, J.: Adendrite method for cluster analysis. Commun. Statist. 1–27 (1974)
Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Prez, J.M., Perona, I.: An extensive comparative study of cluster validity indices. Pattern Recognition 46(1), 243–256 (2013)
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: Clustering validity checking methods: Part II. SIGMOD Record 31(3), 19–27 (2002)
Zhang, B.: Generalized K-Harmonic Means Boosting in Unsupervised Learning. Technical Reports, Hewllet Laborotories, HPL-2000-137 (2000)
Zhang, L., Mao, L., Gong, H., Yang, H.: A K-harmonic Means Clustering Algorithm Based on Enhanced Differential Evolution. In: 2013 Fifth International Conference on Measuring Technology and Mechatronics Automation, 2014 Sixth International Conference on Measuring Technology and Mechatronics Automation, pp. 13–16 (2013)
Thangavel, K., Karthikeyani Visalakshi, K.: Ensemble based Distributed K- Harmonic Means Clustering. International Journal of Recent Trends in Engineering 2(1), 125–129 (2009)
Zhao, Q., Fränti, P.: WB-index: a sum-of-squares based index for cluster validity. Knowledge and Data Engineering 92, 77–89 (2014)
Malinen, M.I., Mariescu-Istodor, R., Fränti K-means*, P.: Clustering by gradual data transformation. Pattern Recognition 47(10), 3376–3386 (2014)
Thomas, J.C.R.: New Version of Davies-Bouldin Index for Clustering Validation Based on Cylindrical Distance. In: V Chilean Workshop on Pattern Recognition, November 11-15 (2013)
Zhang, L., Mao, L., Gong, H., Yang, H.: A K-harmonic Means Clustering Algorithm Based on Enhanced Differential Evolution. In: 2013 Fifth International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), January 16-17, pp. 13–16 (2013), doi:10.1109/ICMTMA.2013.1
Emre, C.M., Kingravi, H.A., Vela, P.A.: A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Systems with Applications (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 IFIP International Federation for Information Processing
About this paper
Cite this paper
Mahi, H., Farhi, N., Labed, K. (2015). Remotely Sensed Data Clustering Using K-Harmonic Means Algorithm and Cluster Validity Index. In: Amine, A., Bellatreche, L., Elberrichi, Z., Neuhold, E., Wrembel, R. (eds) Computer Science and Its Applications. CIIA 2015. IFIP Advances in Information and Communication Technology, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-19578-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-19578-0_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19577-3
Online ISBN: 978-3-319-19578-0
eBook Packages: Computer ScienceComputer Science (R0)