Abstract
In this paper we introduce the Minkowski weighted partition around medoids algorithm (MW-PAM). This extends the popular partition around medoids algorithm (PAM) by automatically assigning K weights to each feature in a dataset, where K is the number of clusters. Our approach utilizes the within-cluster variance of features to calculate the weights and uses the Minkowski metric.
We show through many experiments that MW-PAM, particularly when initialized with the Build algorithm (also using the Minkowski metric), is superior to other medoid-based algorithms in terms of both accuracy and identification of irrelevant features.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Brohee, S., Van Helden, J.: Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics 7(1), 488–501 (2006)
Hartigan, J.A.: Clustering algorithms. John Willey & Sons (1975)
Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recognition Letters 31(8), 651–666 (2010)
Kaufman, L., Rousseeuw, P.J.: Finding groups in data: an introduction to cluster analysis. Wiley Online Library (1990)
Mirkin, B.: Core concepts in data analysis: summarization, correlation and visualization. Springer, New York (2011)
Ball, G.H., Hall, D.J.: A clustering technique for summarizing multivariate data. Behavioral Science 12(2), 153–155 (1967)
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, California, USA, pp. 281–297 (1967)
de Amorim, R.C., Komisarczuk, P.: On partitional clustering of malware. In: CyberPatterns, pp. 47–51. Abingdon, Oxfordshire (2012)
Chan, E.Y., Ching, W.K., Ng, M.K., Huang, J.Z.: An optimization algorithm for clustering using weighted dissimilarity measures. Pattern Recognition 37(5), 943–952 (2004)
Huang, J.Z., Ng, M.K., Rong, H., Li, Z.: Automated variable weighting in k-means type clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 657–668 (2005)
Huang, J.Z., Xu, J., Ng, M., Ye, Y.: Weighting Method for Feature Selection in K-Means. In: Computational Methods of Feature Selection, pp. 193–209. Chapman and Hall (2008)
Mirkin, B.G.: Clustering for data mining: a data recovery approach. CRC Press (2005)
de Amorim, R.C., Mirkin, B.: Minkowski Metric, Feature Weighting and Anomalous Cluster Initializing in K-Means Clustering. Pattern Recognition 45(3), 1061–1075 (2011)
Modha, D.S., Spangler, W.S.: Feature weighting in k-means clustering. Machine Learning 52(3), 217–237 (2003)
Tsai, C.Y., Chiu, C.C.: Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm. Computational Statistics & Data Analysis 52(10), 4658–4672 (2008)
Bilenko, M., Basu, S., Mooney, R.J.: Integrating Constraints and Metric Learning in Semi-Supervised Clustering. In: Proceedings of 21st International Conference on Machine Learning, Banff, Canada, pp. 81–88 (2004)
Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S.: Distance metric learning, with application to clustering with side-information. In: Advances in Neural Information Processing Systems 16, pp. 521–528 (2002)
Makarenkov, V., Legendre, P.: Optimal variable weighting for ultrametric and additive trees and K-means partitioning: Methods and software. Journal of Classification 18(2), 245–271 (2001)
Frigui, H., Nasraoui, O.: Unsupervised learning of prototypes and attribute weights. Pattern Recognition 37(3), 567–581 (2004)
Irvine UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/
Nabney, I., Bishop, C.: Netlab neural network software. Matlab Toolbox
de Amorim, R.C.: Constrained Intelligent K-Means: Improving Results with Limited Previous Knowledge. In: ADVCOMP, pp. 176–180 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Amorim, R.C., Fenner, T. (2012). Weighting Features for Partition around Medoids Using the Minkowski Metric. In: Hollmén, J., Klawonn, F., Tucker, A. (eds) Advances in Intelligent Data Analysis XI. IDA 2012. Lecture Notes in Computer Science, vol 7619. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34156-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-34156-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34155-7
Online ISBN: 978-3-642-34156-4
eBook Packages: Computer ScienceComputer Science (R0)