ClusMAM: fast and effective unsupervised clustering of large complex datasets using metric access methods
Pages 986 - 991
Abstract
An efficient and effective clustering process is a core task of data mining analysis, and has become more important in the nowadays scenario of big data, where scalability is an issue. In this paper we present the ClusMAM method, which proposes a new strategy for clustering large complex datasets through metric access methods. ClusMAM aims at accelerating the process of relational partitional clustering by taking advantage of the inherent node separations of metric access methods. In comparison with other methods from the literature, ClusMAM is up to four orders of magnitude faster than the competitors maintaining clustering quality. Additionally, ClusMAM exploits the datasets to find compact and coherent clusters, suggesting the number of clusters k found in the data. The method was evaluated employing synthetic and real datasets, and the behavior of the method was consistent regarding the number of distance calculations and time required for the clustering process as well.
References
[1]
A. K. Jain, Data clustering: 50 years beyond K-means, Pattern recognition letters, Vol. 31, no. 8, pp. 651--666, 2010.
[2]
R. T. Ng and J. Han, Efficient and effective clustering methods for spatial data mining, VLDB, pp. 144--155, 1994.
[3]
L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley and Sons, 2005.
[4]
M. C. N. Barioni, H. L. Razente, A. J. M. Traina and C. Traina Jr., Accelerating k-medoid-based algorithms through metric access methods, Journal of Systems and Software, vol. 81, no. 3, pp. 342--355, 2008.
[5]
P. Ciaccia, M. Patella and P. Zezula, M-tree: An efficient access method for similarity search in metric spaces, VLDB, pp. 426--435, 1997.
[6]
J. A. de Souza, H. L. Razente and M. C. N. Barioni, Optimizing metric access methods for querying and mining complex data types, Journal of the Brazilian Computer Society, vol. 20, no. 17, pp. 1--14, 2014.
[7]
P. Zezula, G. Amato, V. Dohnal and M. Batko, Similarity Search: The Metric Space Approach, vol. 32, Springer, 2006.
[8]
A. Thomasian, Singular Value Decomposition, Clustering, and Indexing for Similarity Search for Large Data Sets in High-Dimensional Spaces, In BIG DATA Algorithms, Analytics, and Applications, pp. 40--68, CRC, 2015.
[9]
M. Ester, H.-P. Kriegel and X. Xu, Knowledge discovery in large spatial databases: focusing techniques for efficient class identification, SSD, vol. 951, pp. 67--82, 1995.
[10]
J.-J. Hwang, K.-Y. Whang, Y.-S. Moon and B.-S. Lee, A top-down approach for density-based clustering using multidimensional indexes, Journal of Systems and Software, vol. 73, pp. 169--180, 2003.
[11]
H. P. Lai, M. Visani, A. Boucher and J.-M. Ogier, An experimental comparison of clustering methods for content-based indexing of large image databases, Pattern Analysis and Applications, vol. 15, no. 4, pp. 345--366, 2012.
[12]
L. Vendramin, R. J. G. B. Campello and E. R. Hruschka, Relative clustering validiy criteria: A comparative overview, Journal Statistical Analysis and Data Minig, vol. 3, no. 4, pp. 209--235, 2010.
[13]
L. P. S. Avalhais, S. F. da Silva, J. F. Rodrigues Jr., A. J. M. Traina and C. Traina Jr., Feature Space Optimization for Content-Based Image Retrieval, ACM SIGAPP, vol. 12, no. 3, pp. 7--19, 2012.
[14]
I. Färber, S. Günnemann, H-P. Kriegel, P. Kröger, E. Müller, E. Schubert, T. Seidl and A. Zimek, On using class-labels in evaluation of clusterings, MultiClust, 2010.
Index Terms
- ClusMAM: fast and effective unsupervised clustering of large complex datasets using metric access methods
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Published: 04 April 2016
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