Abstract
Unsupervised learning is the process to partition the given data set into number of clusters where similar data objects belongs same cluster and dissimilar data objects belongs to another cluster. k-Means is the partition based unsupervised learning algorithm which is popular for its simplicity and ease of use. Yet, k-Means suffers from the major shortcoming of passing number of clusters and centroids in advance. Decimal scaling is one of the normalization approaches which standardize the features of the dataset and improve the effectiveness of the algorithm. Integrating different distance measures with modified k-Means algorithm help to select the proper distance measure for specific data mining application. This paper compare the results of modified k-Means with different distance measures like Euclidean Distance, Manhattan Distance, Minkowski Distance, Cosine Measure Distance and the Decimal Scaling normalization approach. Result Analysis is taken on various datasets from UCI machine dataset repository and shows that Mk-Means is advantageous and improve the effectiveness with normalized approach and Minkowski distance measure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Velmurugan, T., Santhanam, T.: Computational Complexity between K-Means and K-Medoids Clustering Algorithms for Normal and Uniform Distributions of Data Points. Journal of Computer Science 6 3, 363–368 (2010)
Vimal, A., Valluri, S.R., Karlapalem, K.: An Experiment with Distance Measures for Clustering. In: International Conference on Management of Data, COMAD 2008, Mumbai, India, December 17-19 (2008)
Jiawei Han, M.K.: Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, An Imprint of Elsevier (2006)
Shameem, M.-U.-S., Ferdous, R.: An efficient k-means algorithm integrated with Jaccard distance measure for document clustering. In: First Asian Himalayas International Conference on Internet 2009, AH-ICI 2009, Kathmandu, pp. 1–6 (2009)
Qian, G., Sural, S., Gu, Y., Pramanik, S.: Similarity between Euclidean and Cosine Angle Distance for Nearest Neighbor Queries. In: SAC 2004, Nicosia, Cyprus (March 2004)
Finch, H.: Comparison of Distance Measures in Cluster Analysis with Dichotomous Data. Journal of Data Science 3, 85–100 (2005)
Chang, D.-J., Desoky, A.H., Ouyang, M., Rouchka, E.C.: Compute pairwise Manhattan Distance and Pearson Correlation Coefficient of Data Points with GPU
Visalakshi, N.K., Suguna, J.: k-Means Clustering using Max-min Distance Measure. In: Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2009, Cincinnati, OH, pp. 1–6 (2009)
Wang, L., Bo, L., Jiao, L.: A Modified K-Means Clustering with a Density-Sensitive Distance Metric. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 544–551. Springer, Heidelberg (2006)
Pakhira, M.K.: A Modified k-means Algorithm to Avoid Empty Clusters. International Journal of Recent Trends in Engineering 1(1) (2009)
Xu, R., Wunsch II, D.: Survey of Clustering Algorithms. IEEE Transactions on Neural Networks 16(3) (2005)
Patel, V.R., Mehta, R.G.: Clustering Algorithms: A Comprehensive Survey. In: International Conference on Electronics, Information and Communication Systems Engineering (ICEICE 2010), Jodhpur, Rajasthan (2011)
Karthikeyani Visalakshi, N., Thangavel, K.: Distributed Data Clustering: A Comparative Analysis. Foundations of Computational Intelligence (6), 371–397 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer India Pvt. Ltd.
About this paper
Cite this paper
Patel, V.R., Mehta, R.G. (2012). Data Clustering: Integrating Different Distance Measures with Modified k-Means Algorithm. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_63
Download citation
DOI: https://doi.org/10.1007/978-81-322-0491-6_63
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0490-9
Online ISBN: 978-81-322-0491-6
eBook Packages: EngineeringEngineering (R0)