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Enhancing Ant Brood Clustering with Adaptive Radius of Perception and Non-parametric Estimation on Multi-core Architectures

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Advances in Intelligent Networking and Collaborative Systems (INCoS 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 8))

Abstract

Clustering is an important problem in complex networks. Exact algorithmic approaches to clustering is not affordable for many real world instances, requiring innovative, approximation algorithms. Among them are meta-heuristics such as nature-inspired techniques. One of the existing techniques inspired by real ants in nature, is called ant brood clustering algorithm (ACA). In this paper, we present Ant Clustering Algorithm with Adaptive Radius (ACA-AR). Unlike existing ACA Models, ACA-AR utilizes Kernel Density Estimation (KDE) to measure average dissimilarity of data objects in ant’s neighborhood, and it allows ants to adapt the radius of perception so they can avoid the convergence to a local-optimum. We also present a parallel counterpart of the algorithm on the Graphics Processing Unit (GPU) using NVIDIA CUDA and on multi-core CPU cores using OpenMP. Our results on benchmark datasets show that ACA-AR gains substantial clustering accuracy, and the parallel version executes up to 39x faster whilst preserving the quality of the retrieved clusters.

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Authors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    Mohmmed Qasem, Ying Ying Liu, Ziyue Wang, Parimala Thulasiraman & Ruppa K. Thulasiram

Authors
  1. Mohmmed Qasem
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  2. Ying Ying Liu
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  3. Ziyue Wang
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  4. Parimala Thulasiraman
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  5. Ruppa K. Thulasiram
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Corresponding author

Correspondence to Parimala Thulasiraman .

Editor information

Editors and Affiliations

  1. Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, Ryerson University, Toronto, Ontario, Canada

    Isaac Woungang

  3. School of Business, UNSW Australia, Canberra, Aust Capital Terr, Australia

    Omar Khadeer Hussain

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Qasem, M., Liu, Y.Y., Wang, Z., Thulasiraman, P., Thulasiram, R.K. (2018). Enhancing Ant Brood Clustering with Adaptive Radius of Perception and Non-parametric Estimation on Multi-core Architectures. In: Barolli, L., Woungang, I., Hussain, O. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-65636-6_27

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  • DOI: https://doi.org/10.1007/978-3-319-65636-6_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65635-9

  • Online ISBN: 978-3-319-65636-6

  • eBook Packages: EngineeringEngineering (R0)

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