A Novel Artificial Immune Algorithm for Spatial Clustering with Obstacle Constraint and Its Applications

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 prese...

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Published in:	Computational Intelligence and Neuroscience Vol. 2014; no. 2014; pp. 37 - 47
Main Authors:	Zhang, Ji, Ding, Xintao, Luo, Yonglong, Sun, Liping
Format:	Journal Article
Language:	English
Published:	Cairo, Egypt Hindawi Limiteds 01.01.2014 Hindawi Publishing Corporation John Wiley & Sons, Inc
Subjects:	Algorithms Approximation Artificial Intelligence Cluster Analysis Clustering Comparative analysis Computational Biology Economic models Experiments Geospatial data Hogs Humans Immune system Immune System - physiology Obstacles Operations research Optimization Similarity
ISSN:	1687-5265, 1687-5273, 1687-5273
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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.
AbstractList	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. 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.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.
Audience	Academic
Author	Luo, Yonglong Ding, Xintao Sun, Liping Zhang, Ji
AuthorAffiliation	1 College of National Territorial Resources and Tourism, Anhui Normal University, China 2 Engineering Technology Research Center of Network and Information Security, Anhui Normal University, China 3 Faculty of Health, Engineering and Sciences, University of Southern Queensland, Australia
AuthorAffiliation_xml	– name: 3 Faculty of Health, Engineering and Sciences, University of Southern Queensland, Australia – name: 2 Engineering Technology Research Center of Network and Information Security, Anhui Normal University, China – name: 1 College of National Territorial Resources and Tourism, Anhui Normal University, China
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/25435862$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1016/j.resconrec.2012.12.010 10.1016/j.patrec.2010.09.013 10.1016/j.neucom.2013.02.007 10.1002/9780470316801 10.1016/j.isprsjprs.2003.12.003 10.1016/j.pnsc.2007.10.012 10.1016/j.ins.2008.10.034 10.1016/j.asoc.2011.08.042 10.1016/j.eswa.2011.08.064 10.1023/A:1009745219419 10.1016/j.cie.2012.12.001 10.1007/s10844-011-0154-7 10.1016/j.pnsc.2008.08.004 10.1016/j.ijepes.2010.06.019 10.1016/j.neucom.2012.08.022 10.1016/j.amc.2006.05.166 10.1016/j.eswa.2008.05.005 10.1016/j.asoc.2010.10.017 10.1016/j.cageo.2013.03.002 10.1016/0020-0190(72)90045-2
ContentType	Journal Article
Copyright	Copyright © 2014 Liping Sun et al. COPYRIGHT 2014 John Wiley & Sons, Inc. Copyright © 2014 Liping Sun et al. Liping Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2014 Liping Sun et al. 2014
Copyright_xml	– notice: Copyright © 2014 Liping Sun et al. – notice: COPYRIGHT 2014 John Wiley & Sons, Inc. – notice: Copyright © 2014 Liping Sun et al. Liping Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: Copyright © 2014 Liping Sun et al. 2014
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Snippet	An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional...
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StartPage	37
SubjectTerms	Algorithms Approximation Artificial Intelligence Cluster Analysis Clustering Comparative analysis Computational Biology Economic models Experiments Geospatial data Hogs Humans Immune system Immune System - physiology Obstacles Operations research Optimization Similarity
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Title	A Novel Artificial Immune Algorithm for Spatial Clustering with Obstacle Constraint and Its Applications
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Volume	2014
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