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A Clustering Algorithm for Multi-density Datasets

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Intelligent Computing Paradigm and Cutting-edge Technologies (ICICCT 2019)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 9))

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Abstract

DBSCAN algorithm discovers clusters of various shapes and sizes. But it fails to discover clusters of different density. This is due to its dependency on global value for Eps. This paper introduces an idea to deal with this problem. The offered method estimates local density for a point as the sum of distances to its k-nearest items, arranges items in ascending order according to their local density. The clustering process is started from the highest density point by adding un-clustered points that have similar density as first point in cluster. Also, the point is assigned to current cluster if the sum of distances to its Minpts-nearest neighbors is less than or equal to the density of first point (core point condition in DBSCAN). Experimental results display the efficiency of the proposed method in discovering varied density clusters from data.

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Acknowledgements

This project was supported by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University under the research project no. 2017/01/7120.

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Authors and Affiliations

  1. Faculty of Sciences and Humanitarian Study, Prince Sattam Bin Abdulaziz University, Al-Aflaj, Saudi Arabia

    Ahmed Fahim

  2. Faculty of Computers and Information, Suez University, Suez, Egypt

    Ahmed Fahim

Authors
  1. Ahmed Fahim
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Corresponding author

Correspondence to Ahmed Fahim .

Editor information

Editors and Affiliations

  1. University of Technology Sydney, Sydney, Australia

    Lakhmi C. Jain

  2. CSIE Department, National Dong Hwa University, New Taipei City, Taiwan

    Sheng-Lung Peng

  3. Al-Balqa’ Applied University, Salt, Jordan

    Basim Alhadidi

  4. Department of Computer Science, Brainware University, Kolkata, West Bengal, India

    Souvik Pal

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Fahim, A. (2020). A Clustering Algorithm for Multi-density Datasets. In: Jain, L., Peng, SL., Alhadidi, B., Pal, S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2019. Learning and Analytics in Intelligent Systems, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-38501-9_2

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