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A fuzzy version of generalized DBSCAN clustering algorithm

Published: 18 March 2015 Publication History

Abstract

In this paper, we propose a fuzzy version of GDBSCAN called generalized fuzzy density based clustering algorithm (GFDBSCAN) that can be used to cluster people around key socio-economic parameters. GFDBSCAN can also be used to cluster geographical regions based on the requirement and preferences expressed by the customers for services like business outlets, ATMs, bank branch operations, public utilities, etc. We apply the proposed algorithm to cluster geo-spatial data based on personal traits of people living in a given geographical area. We compare the performance of GFDBSCAN with other popular clustering algorithms and measure the performance of the algorithms using silhouette coefficient.

References

[1]
C. C. Aggarwal and P. S. Yu. A survey of uncertain data algorithms and applications. IEEE Trans. On Knowl. and Data Eng., 21(5): 609--623, May 2009.
[2]
J. Sander, M. Ester, H.-P. Kriegel, and X. Xu. Density based clustering in spatial databases: The algorithm gdbscan and its applications. Data Mining and Knowledge Discovery, 2(2): 169--194, jun 1998.

Cited By

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  • (2022)A Literature survey based on DBSCAN algorithms2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS)10.1109/ICICCS53718.2022.9788440(751-758)Online publication date: 25-May-2022
  • (2017)An adaptive density clustering algorithm for massive data2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/FSKD.2017.8393022(1700-1707)Online publication date: Jul-2017
  • (2015)DBSCAN-MProceedings, Part II, of the 15th International Conference on Algorithms and Architectures for Parallel Processing - Volume 952910.1007/978-3-319-27122-4_5(66-77)Online publication date: 18-Nov-2015

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Published In

cover image ACM Other conferences
CODS '15: Proceedings of the 2nd ACM IKDD Conference on Data Sciences
March 2015
150 pages
ISBN:9781450334365
DOI:10.1145/2732587
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2015

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Author Tags

  1. ATM location
  2. clustering algorithms
  3. density based
  4. fuzzy

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CODS '15
CODS '15: 2nd IKDD Conference on Data Sciences
March 18 - 21, 2015
Bangalore, India

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Overall Acceptance Rate 197 of 680 submissions, 29%

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Cited By

View all
  • (2022)A Literature survey based on DBSCAN algorithms2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS)10.1109/ICICCS53718.2022.9788440(751-758)Online publication date: 25-May-2022
  • (2017)An adaptive density clustering algorithm for massive data2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/FSKD.2017.8393022(1700-1707)Online publication date: Jul-2017
  • (2015)DBSCAN-MProceedings, Part II, of the 15th International Conference on Algorithms and Architectures for Parallel Processing - Volume 952910.1007/978-3-319-27122-4_5(66-77)Online publication date: 18-Nov-2015

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