Abstract
In this work we describe the application of the LVA-Index in the NBC algorithm and discuss the results of the relevant experiments. LVA-Index is based on the idea of approximation vectors and the layer approach. NBC is considered as an efficient density-based clustering algorithm. The efficiency of NBC is strictly dependent on the efficiency of determining nearest neighbors. For this reason, the authors of NBC used the simplified implementation of the VA-File and the idea of layers for indexing points and determining nearest neighbors. We noticed that is possible to speed up the clustering by applying the LVA-Index which provides the means for determining nearest neighbors faster. The results of the experiments prove that incorporating the LVA-Index into the NBC improves the efficiency of clustering.
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
Han, J., Kamber, M.: Data mining: concepts and techniques. Morgan Kaufmann Publishers (2000)
Zhang, Q., et al.: BIRCH: A new data clustering algorithm and its applications. Data Mining and Knowledge Discovery 1(2), 141–182 (1997)
Guha, S., Rastogi, R., Shim, K.: CURE: an efficient clustering algorithm for large databases. In: SIGMOD 1998: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, pp. 73–84 (1998)
Ng, R., Han, J.: CLARANS: A Method for Clustering Objects for Spatial Data Mining. In: CLARANS: A Method for Clustering Objects for Spatial Data Mining, pp. 1003–1016 (2002)
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 International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Ankerst, M., Breunig, M., Kriegel, H., Sander, J.: OPTICS: Ordering Points To Identify the Clustering Structure. In: SIGMOD Conference, pp. 49–60 (1999)
Wang, W., Yang, J., Muntz, R.R.: STING: A Statistical Information Grid Approach to Spatial Data Mining. In: Proceedings of the 23rd International Conference on Very Large Data Bases, August 25-29, pp. 186–195 (1997)
Zhou, S., Zhao, Y., Guan, J., Huang, J.: A neighborhood-based clustering algorithm. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 361–371. Springer, Heidelberg (2005)
Weber, R., Schek, H.J., Blott, S.: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: Proceedings of the 24th VLDB Conference on Very Large Data Bases(VLDB 1998), New York City, NY, pp. 194–205 (1998)
Lasek, P.: Efficient Density-Based Clustering. Ph.D. Thesis, Wydawnictwo Politechniki Warszawskiej (2011)
Lasek, P.: LVA-Index: an Effcient Way to Determine Nearest Neighbors. In: Man Machine Interactions, ICMMI (2009)
Kryszkiewicz, M., Lasek, P.: A neighborhood-based clustering by means of the triangle inequality. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds.) IDEAL 2010. LNCS, vol. 6283, pp. 284–291. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Lasek, P. (2013). The LVA-Index in Clustering. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) New Results in Dependability and Computer Systems. Advances in Intelligent Systems and Computing, vol 224. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00945-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-00945-2_24
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00944-5
Online ISBN: 978-3-319-00945-2
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)