NDLI: An approximation algorithm for finding skeletal points for density based clustering approaches

Content Provider	IEEE Xplore Digital Library
Author	Yeganeh, S.H. Habibi, J. Abolhassani, H. Tehrani, M.A. Esmaelnezhad, J.
Copyright Year	2009
Description	Author affiliation: Computer Engineering Department, Sharif University of Techonology, Azadi Ave, Tehran, Iran (Yeganeh, S.H.; Habibi, J.; Abolhassani, H.; Tehrani, M.A.; Esmaelnezhad, J.)
Abstract	Clustering is the problem of finding relations in a data set in an supervised manner. These relations can be extracted using the density of a data set, where density of a data point is defined as the number of data points around it. To find the number of data points around another point, region queries are adopted. Region queries are the most expensive construct in density based algorithm, so it should be optimized to enhance the performance of density based clustering algorithms specially on large data sets. Finding the optimum set of region queries to cover all the data points has been proven to be NP-complete. This optimum set is called the skeletal points of a data set. In this paper, we proposed a generic algorithms which fires region queries at most 6 times the optimum number of region queries (has 6 as approximation factor). Also, we have extend this generic algorithm to create a DBSCAN (the most wellknown density based algorithm) derivative, named ADBSCAN. Presented experimental results show that ADBSCAN has a better approximation to DBSCAN than the DBRS-H (the most well-known randomized density based algorithm) in terms of performance and quality of clustering, specially for large data sets.
Starting Page	403
Ending Page	410
File Size	1384140
Page Count	8
File Format	PDF
ISBN	9781424427659
DOI	10.1109/CIDM.2009.4938678
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Information retrieval Extraterrestrial measurements Data Mining Approximation Algorithms Clustering Information systems Databases Clustering algorithms Euclidean distance Approximation algorithms Impedance Web sites Logic Spatial Clustering
Content Type	Text
Resource Type	Article

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