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

Hassas Yeganeh, S ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1109/CIDM.2009.4938678
  3. Publisher: 2009
  4. Abstract:
  5. 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 well-known 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. © 2009 IEEE
  6. Keywords:
  7. Clustering ; Data mining ; Spatial clustering ; Approximation factor ; Clustering ; Data points ; Data sets ; Density-based algorithm ; Density-based Clustering ; Density-based clustering algorithms ; Generic algorithm ; Large datasets ; NP Complete ; Number of datum ; Optimum number ; Quality of clustering ; Artificial intelligence ; Clustering algorithms ; Mining ; Optimization ; Set theory ; Approximation algorithms
  8. Source: 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009, Nashville, TN, 30 March 2009 through 2 April 2009 ; 2009 , Pages 403-410 ; 9781424427659 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4938678/authors#authors