Abstract
Analyzing massive amounts of data and extracting value from it has become key across different disciplines. As the amounts of data grow rapidly, however, current approaches for data analysis struggle. This is particularly true for clustering algorithms where distance calculations between pairs of points dominate overall time.
Crucial to the data analysis and clustering process, however, is that it is rarely straightforward. Instead, parameters need to be determined through several iterations. Entirely accurate results are thus rarely needed and instead we can sacrifice precision of the final result to accelerate the computation. In this paper we develop ADvaNCE, a new approach to approximating DBSCAN. ADvaNCE uses two measures to reduce distance calculation overhead: (1) locality sensitive hashing to approximate and speed up distance calculations and (2) representative point selection to reduce the number of distance calculations. Our experiments show that our approach is in general one order of magnitude faster (at most 30x in our experiments) than the state of the art.
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
Similar content being viewed by others
References
Adaszewski, S., Dukart, J., Kherif, F., Frackowiak, R., Draganski, B.: How early can we predict Alzheimer’s disease using computational anatomy? Neurobiol. Aging 34(12), 2815–2826 (2013)
Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun. ACM 51(1), 117–122 (2008)
Ankerst, M., Breunig, M.M., Kriegel, H.-P., Sander, J.: OPTICS: ordering points to identify the clustering structure. In: SIGMOD 1999 (1999)
Bache, K., Lichman, M.: UCI Machine Learning Repository (2013)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)
Borah, B., Bhattacharyya, D.: An improved sampling-based DBSCAN for large spatial databases. In: Conference on Intelligent Sensing and Information Processing (2004)
Chen, M.-S., Han, J., Yu, P.: Data mining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8(6), 866–883 (1996)
Collins, L.M., Dent, C.W.: Omega: a general formulation of the rand index of cluster recoverysuitable for non-disjoint solutions. Multivar. Behav. Res. 23(2), 231–242 (1988)
Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, SCG 2004 (2004)
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 and Data Mining (1996)
Gan, J., Tao, Y.: DBSCAN revisited: mis-claim, un-fixability, and approximation. In: SIGMOD 2015 (2015)
Gunawan, A.: A faster algorithm for DBSCAN. Master’s thesis, Technical University of Eindhoven, March 2013
Patwary, M., Ali, M., Satish, N., Sundaram, N., Manne, F., Habib, S., Dubey, P.: Pardicle: parallel approximate density-based clustering. In: Supercomputing 2014 (2014)
Viswanath, P., Pinkesh, R.: l-DBSCAN: a fast hybrid density based clustering method. In: Proceedings of the Conference on Pattern Recognition (2006)
Yeganeh, S., Habibi, J., Abolhassani, H., Tehrani, M., Esmaelnezhad, J.: An approximation algorithm for finding skeletal points for density based clustering approaches. In: Symposium on Computational Intelligence and Data Mining (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, T., Heinis, T., Luk, W. (2016). Hashing-Based Approximate DBSCAN. In: Pokorný, J., Ivanović, M., Thalheim, B., Šaloun, P. (eds) Advances in Databases and Information Systems. ADBIS 2016. Lecture Notes in Computer Science(), vol 9809. Springer, Cham. https://doi.org/10.1007/978-3-319-44039-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-44039-2_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44038-5
Online ISBN: 978-3-319-44039-2
eBook Packages: Computer ScienceComputer Science (R0)