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Comparative Study of Euclidean and City Block Distances in Fuzzy C-Means Clustering Algorithm


Affiliations
1 University Sains Malaysia, School of Mathematical Sciences, Pinang, Malaysia.
2 University Sains Malaysia, School of Mathematical Sciences, Pinang, Malaysia
 

Fuzzy c-means algorithm is one of the most important partitioning techniques and widely used for data clustering and image segmentation. The choice of distance metrics has played key role in data clustering problems since distance metric is used to determine the similarities between data points. In this paper Fuzzy c-means algorithms uses Euclidean and City block distances for comparative analysis to measure the similarities between objects. The results for data clustering problems using Euclidean distance has shown good performance than City block distance in terms of computational time values and the quality of clusters obtained. Similarities, differences and applications of the two proposed distance metrics have been described.
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  • Grabusts, P. The choice of metrics for clustering algorithms. In Proceedings of the 8th International Scientific and Practical Conference (Vol. 2). ISSN 1691-5402 ISBN 978-9984-44-071-2 Pp. 70 -76, 2011.

  • Jafar, O. M., & Sivakumar, R. Hybrid Fuzzy Data Clustering Algorithm Using Different Distance Metrics: A Comparative Study. International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume 3, Issue 6, pp. 241 – 248, January 2014.

  • De Carvalho, F. D. A., & Tenório, C. P. Partitioning Fuzzy c-means clustering for interval-valued data based on adaptive quadratic distances. Fuzzy Sets and Systems, 161(23), 2978-2999, 2010.

  • De Carvalho, F. D. A. A fuzzy clustering algorithm for symbolic interval data based on a single adaptive Euclidean distance. In Neural Information Processing. RAMEPublishers Berlin Heidelberg. , pp. 1012-1021, January, 2006.

  • De Carvalho, F. D. A., Bertrand, P., & De Melo, F. M.. Batch self-organizing maps based on city-block distances for interval variables.Version 1, pp1-15, 2012.

  • Lu, Y., Ma, T., Yin, C., Xie, X., Tian, W., & Zhong, S.. Implementation of the Fuzzy C-Means Clustering Algorithm in Meteorological Data. International Journal of Database Theory and Application, Vol.6, No.6. Pp.1-18, 2013.

  • Ranunal, J. R., & Dorado, J. Artificial Neural Networks in Real-Life Applications. Idea Group Publishing, Hershey, USA. ISBN: 1-59140-902-0, 2006.

  • Hasnat, A., Halder, S., Bhattacharjee, D., Nasipuri, M., & Basu, D. K.. Comparative Study of Distance Metrics for Finding Skin Color Similarity of Two Color Facial Images. Computer Science & Information Technology, pp. 99-108, 2013.

  • Kimmel, R., Shaked, D., Kiryati, N., and Bruckstein, A. M., 1996.

  • Eugene, F. K, 1987.


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  • Comparative Study of Euclidean and City Block Distances in Fuzzy C-Means Clustering Algorithm

Abstract Views: 140  |  PDF Views: 95

Authors

Saratha Sathasivam
University Sains Malaysia, School of Mathematical Sciences, Pinang, Malaysia.
Abdu Masanawa Sagir
University Sains Malaysia, School of Mathematical Sciences, Pinang, Malaysia

Abstract


Fuzzy c-means algorithm is one of the most important partitioning techniques and widely used for data clustering and image segmentation. The choice of distance metrics has played key role in data clustering problems since distance metric is used to determine the similarities between data points. In this paper Fuzzy c-means algorithms uses Euclidean and City block distances for comparative analysis to measure the similarities between objects. The results for data clustering problems using Euclidean distance has shown good performance than City block distance in terms of computational time values and the quality of clusters obtained. Similarities, differences and applications of the two proposed distance metrics have been described.

References