Sun Liping, Luo Yonglong, Ding Xintao, Zhang Ji
College of National Territorial Resources and Tourism, Anhui Normal University, China ; Engineering Technology Research Center of Network and Information Security, Anhui Normal University, China.
Engineering Technology Research Center of Network and Information Security, Anhui Normal University, China.
Comput Intell Neurosci. 2014;2014:160730. doi: 10.1155/2014/160730. Epub 2014 Nov 4.
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.
空间聚类算法的一个重要组成部分是对象空间中样本点之间的距离度量。在本文中，传统的欧几里得距离度量被创新的障碍物距离度量所取代，用于障碍物约束下的空间聚类。首先，我们提出一种路径搜索算法来近似两点之间的障碍物距离，以处理障碍物和促进因素。将障碍物距离作为相似性度量，随后我们提出了带障碍物实体的人工免疫聚类（AICOE）算法，用于在存在障碍物和促进因素的情况下对空间点数据进行聚类。最后，本文对AICOE算法和经典聚类算法进行了对比分析。我们基于人工免疫系统的聚类模型还应用于公共设施选址问题的案例，以确立我们方法的实际适用性。通过使用克隆选择原理并基于精英抗体更新聚类中心，AICOE算法能够实现全局最优和更好的聚类效果。
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