关键词: 初始聚类中心, K-均值算法, 集群度, 距离均衡优化, 文本聚类

Abstract: To deal with the problem that the traditional K-means algorithm is sensitive to the initial clustering center selection, an algorithm of K-Means clustering based on Clustering degree and Distance equalization optimization (K-MCD) was proposed. Firstly, the initial clustering center was selected based on the idea of "cluster degree". Secondly, the selection strategy of total clustering center distance equilibrium optimization was followed to obtain the final initial clustering center. Finally, the text set was vectorized, and the text cluster center and the evaluation criteria of text clustering were reselected to perform text clustering analysis according to the optimization algorithm. The analysis of simulation experiment for the text data set was carried out from the aspects of accuracy and stability. Compared with K-means algorithm, the clustering accuracy of K-MCD algorithm was improved by 18.6, 17.5, 24.3 and 24.6 percentage points respectively for four text sets; the average evolutionary algebraic variance of K-MCD algorithm was 36.99 percentage points lower than K-means algorithm. The experimental results show that K-MCD algorithm can improve text clustering accuracy with good stability.

Key words: initial clustering center, K-means algorithm, cluster degree, distance equalization optimization, text clustering