Abstract
As data mining having become increasingly important, clustering algorithms with lots of applications have attracted a significant amount of research attention in recent decades. There are many different clustering techniques having been proposed. Some conventional partitioning-based clustering methods, such as K-means, may fail if a set of incorrect parameters is chosen, or breakdown when the objects consist of non-spherical patterns. Although density-based approaches, e.g. DBSCAN and IDBSCAN, could deliver better results, they may increase time cost when using large data bases. In this investigation, a new clustering algorithm termed DenVOICE is provided to circumvent the problems stated above. As a hybrid technique that combines density-partitioning clustering concept, the proposed algorithm is capable of resulting in precise pattern recognition while decreasing time cost. Experiments illustrate that the new algorithm can recognize arbitrary patterns, and efficiently eliminate the problem of long computational time when employing large data bases. It also indicates that the proposed approach produces much smaller errors than K-means, DBSCAN and IDBSCAN techniques in most the cases examined herein.
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
McQueen, J.B.: Some Methods of Classification and Analysis of Multivariate Observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Hinneburg, A., Keim, D.A.: An Efficient Approach to Clustering in Large Multimedia Databases with Noise. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 58–65 (1998)
Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. pp. 94–105. ACM Press, Seattle (1998)
Karypis, G., Han, E.-H., Kumar, V.: CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. IEEE Computer, 68–75 (1999)
Krishna, K., Murty, M.N.: Genetic k-means algorithm. IEEE Transactions on Systems, Man, and Cybernetics-Part B: CYBERNETICS 29, 433–439 (1999)
Bandyopadhyay, S., Maulik, U.: An Evolutionary Technique Based on K-means Algorithm for Optimal Clustering in RN. Information Sciences 146, 221–237 (2002)
Borah, B., Bhattacharyya, D.K.: An Improved Sampling-Based DBSCAN for Large Spatial Databases. In: Proceedings of International Conference on Intelligent Sensing and Information, pp. 92–96 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chang, JF. (2010). DenVOICE: A New Density-Partitioning Clustering Technique Based on Congregation of Dense Voronoi Cells for Non-spherical Patterns. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6421. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16693-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-16693-8_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16692-1
Online ISBN: 978-3-642-16693-8
eBook Packages: Computer ScienceComputer Science (R0)