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MSDBSCAN: Multi-density scale-independent clustering algorithm based on DBSCAN

Esfandani, G ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-642-17316-5_19
  3. Publisher: 2010
  4. Abstract:
  5. A good approach in data mining is density based clustering in which the clusters are constructed based on the density of shape regions. The prominent algorithm proposed in density based clustering family is DBSCAN [1] that uses two global density parameters, namely minimum number of points for a dense region and epsilon indicating the neighborhood distance. Among others, one of the weaknesses of this algorithm is its un-suitability for multi-density data sets where different regions have various densities so the same epsilon does not work. In this paper, a new density based clustering algorithm, MSDBSCAN, is proposed. MSDBSCAN uses a new definition for core point and dense region. The MSDBSCAN can find clusters in multi-variant density data sets. Also this algorithm benefits scale independency. The results obtained on data sets show that the MSDBSCAN is very effective in multi-variant environment
  6. Keywords:
  7. Data sets ; Dense region ; Density data ; Density parameters ; Density-based Clustering ; Density-based clustering algorithms ; local core distance ; MSDBSCAN ; Multi-density scale-independent clustering ; Scale independency ; Various densities ; Data mining ; Clustering algorithms
  8. Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 19 November 2010 through 21 November 2010, Chongqing ; Volume 6440 LNAI, Issue PART 1 , November , 2010 , Pages 202-213 ; 03029743 (ISSN) ; 3642173152 (ISBN)
  9. URL: http://link.springer.com/chapter/10.1007%2F978-3-642-17316-5_19