A Novel Artificial Immune Algorithm for Spatial Clustering with Obstacle Constraint and Its Applications. (4th November 2014)

• Record Type: Journal Article
• Authors: Sun, Liping; Luo, Yonglong; Ding, Xintao; Zhang, Ji
• Other Names: Yang Jianjun Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2014(2014)
• Publication Date: 2014-11-04
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2014/160730
• Languages: English
• ISSNs: 1687-5265

Abstract

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.