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A Multi-purpose Density Based Clustering Framework

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Contemporary Computing (IC3 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 168))

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Abstract

In this paper, we present a multi-purpose density-based clustering framework. The framework is based on a novel cluster merging algorithm which can efficiently merge two sets of DBSCAN clusters using the concept of intersection points. It is necessary and sufficient to process just the intersection points to merge clusters correctly. The framework allows for clustering data incrementally, parallelizing the DBSCAN algorithm for clustering large data sets and can be extended for clustering streaming data. The framework allows us to see the clustering patterns of the new data points separately. Results presented in the paper establish the efficiency of the proposed incremental clustering algorithm in comparison to IncrementalDBSCAN algorithm. Our incremental algorithm is capable of adding points in bulk, whereas IncrementalDBSCAN adds points, one at a time.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science & Information Systems, BITS, Pilani, India

    Navneet Goyal, Poonam Goyal, Mayank P. Mohta, Aman Neelappa & K. Venkatramaiah

Authors
  1. Navneet Goyal
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  2. Poonam Goyal
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  3. Mayank P. Mohta
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  4. Aman Neelappa
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  5. K. Venkatramaiah
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Editor information

Editors and Affiliations

  1. Iowa State University and IIT Bombay, India, 329 Durham, Ames, IA 5001, Iowa, USA

    Srinivas Aluru

  2. Indian Statistical Institute, 203 B.T. Road, 700 108, Kolkata, West Bengal, India

    Sanghamitra Bandyopadhyay

  3. The Ohio State University, 3190 Graves Hall, 333 W 10th Ave, 43210, Columbus, OH, USA

    Umit V. Catalyurek

  4. Department of Computing Science, Chalmers University, Rännvagen 6B, 412 96, Göteborg, Sweden

    Devdatt P. Dubhashi

  5. Dept. of Electrical and Computer Engineering, Iowa State University, 329 Durham, IA 50011, Ames, USA

    Phillip H. Jones

  6. TASSL, Dept. of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Brett Road, NJ 08854-8058, Piscataway, USA

    Manish Parashar

  7. School of Computer Engineering, Nanyang Technological University, N4-02a-32 Nanyang Ave, 639798, Singapore

    Bertil Schmidt

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© 2011 Springer-Verlag Berlin Heidelberg

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Cite this paper

Goyal, N., Goyal, P., Mohta, M.P., Neelappa, A., Venkatramaiah, K. (2011). A Multi-purpose Density Based Clustering Framework. In: Aluru, S., et al. Contemporary Computing. IC3 2011. Communications in Computer and Information Science, vol 168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22606-9_54

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  • DOI: https://doi.org/10.1007/978-3-642-22606-9_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22605-2

  • Online ISBN: 978-3-642-22606-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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