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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Veröffentlicht in:Computational Intelligence and Neuroscience Jg. 2014; H. 2014; S. 37 - 47
Hauptverfasser: Zhang, Ji, Ding, Xintao, Luo, Yonglong, Sun, Liping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Limiteds 01.01.2014
Hindawi Publishing Corporation
John Wiley & Sons, Inc
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Academic Editor: Jianjun Yang
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2014/160730