一种基于蜂群原理的划分聚类算法*

针对现有的大部分划分聚类算法受聚类簇的个数K的限制，提出一种基于蜂群原理的划分聚类算法。该方法通过引入蜂群采蜜机制，将聚类中心视为食物源，通过寻找食物源的自组织过程来实现数据对象的聚集。在聚类的过程中引入紧密度函数来评价聚类中心（局部），引入分离度函数来确定最佳聚类簇的个数（全局）。与传统的划分聚类算法相比，本算法无须指定聚类个数即可实现聚类过程。通过仿真实验表明，本文提出的算法不但对最佳聚类数有良好的搜索能力，而且有较高的准确率：算法时间复杂度仅为O（n*k3）(k<

Partition clustering algorithm based on artificial bee colony principal

according to the drawback that most of these algorithms have the shortcoming that clustering results are limited by K value which is the number of clusters, this paper proposed a new partition clustering algorithm based on the principal of artificial bee colony. The clustering method introduces the mechanism of artificial bee colony collecting pollen and every clustering center will be considered as a food source.Then the process of gathering data objects will be achieved by the process of finding the food source. In the process of clustering, tightness function is proposed as the fitness to evaluate the cluster center(local) and separation function is introduced to determine the optimal number of clusters (global). Comapared to traditional partition clustering algorithms, this algorithm does not need the value K that is a given number of clusters to realize clustering process. Simulation results show that the algorithm not only can determine the best number of clusters, and can get a higher clustering accuracy. Furthermore, the time complexity of this algorithm is O(n*k3)(k<