基于进化思想的聚类算法及其类簇融合算法

针对K均值聚类算法对类簇数目预先不可知及无法处理非凸形分布数据集的缺陷, 提出基于进化思想的聚类算法及其类簇融合算法, 该算法将K均值聚类算法嵌入进化聚类算法框架中, 通过调整距离倍参, 将数据逐渐划分, 在此过程中自动确定类簇数目, 提出基于最近距离的中间圆密度簇融合算法和基于代表类的中间圆密度簇融合算法, 将相似度大的类簇进行融合, 使得k值逐渐趋向真实值. 实验表明, 该方法具有良好的实用性.

Clustering Algorithm based on Evolutionary Thought and Its Cluster Fusion Algorithm

Aiming at the defects of K-means clustering algorithm that the number of clusters is unknown in advance and cannot deal with non-convex distributed data sets, a clustering algorithm based on evolutionary idea and its cluster fusion algorithm are proposed, The algorithm embeds the K-means clustering algorithm into the framework of evolutionary clustering algorithm. By adjusting the distance doubling parameter, the data objects will be divided gradually, and the number of clusters k will be determined adaptively, Then, a middle circle density cluster fusion algorithm based on the nearest distance and a middle circle density cluster fusion algorithm based on representative classes are proposed to fuse the clusters with high similarity, so that the k value gradually tends to the real value. Experiments show that this method has good practice.