A novel artificial immune algorithm for spatial clustering with obstacle constraint and its applications

Sun, Liping and Luo, Yonglong and Ding, Xintao and Zhang, Ji (2014) A novel artificial immune algorithm for spatial clustering with obstacle constraint and its applications. Computational Intelligence and Neuroscience, 2014. ISSN 1687-5265

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Abstract

An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE) algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2014 Liping Sun et al. This is an open access article distributed under the Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 08 Apr 2015 23:28
Last Modified: 01 Mar 2016 23:34
Uncontrolled Keywords: data mining; spatial clustering analysis; datasets; linear obstacles; planar obstacles; geospatial
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0804 Data Format > 080401 Coding and Information Theory
08 Information and Computing Sciences > 0803 Computer Software > 080301 Bioinformatics Software
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1155/2014/160730
URI: http://eprints.usq.edu.au/id/eprint/26759

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