关键词: K-means, 精确度, 信息熵, 属性赋权, 初始聚类中心

Abstract: Concerning the initial clustering center generation and the data similarity judgment basis of the traditional K-means algorithm, the paper proposed an accurate property weighted K-means clustering algorithm based on information entropy to further improve the clustering accuracy. First, property weights were determined by using entroy method to correct the Euclidean distance. And then, high-quality initial clustering center was chosen by comparing the empowering target cost function of the initial clusters for more accurate and more stable clustering. Finally, the algorithm was implemented in Matlab. The experimental results show that the algorithm accuracy and stability are significantly higher than the traditional K-means algorithm.

Key words: K-means, accuracy, information entropy, property weight, initial clustering center