Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (14): 95-102.DOI: 10.3778/j.issn.1002-8331.2006-0318

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Study on [K]-means Clustering Algorithm of Quadratic Power Coupling

XIANG Yixuan, JIANG He, PAN Pinchen, SUN Conghui   

  1. School of Computer Science and Technology, Qilu University of Technology(Shandong Academy of Sciences), Jinan 250353, China
  • Online:2021-07-15 Published:2021-07-14



  1. 齐鲁工业大学(山东省科学院) 计算机科学与技术学院,济南 250353


In clustering research, it is generally believed that the objects, attributes and other aspects of data sets are independent and identically distributed, and they do not affect each other. However, in fact, there are some potential relations between them, namely, Non-IID. In order to better mine the potential relationship, the data set is processed by the second power, and the data set samples coupled by the second power are obtained after calculating Pearson correlation coefficient. In order to solve the sensitivity problem of [K]-means clustering algorithm in selecting the initial center point, based on the idea of density, the high-density region is reasonably adjusted by calculating the density parameters, The clustering iteration method is used to select the points. The maximum density point in the high-density region is taken as the initial point, the farthest point from the initial point is taken as the second point, and the previous two points are taken as the center. Two centroids are obtained by clustering iteration, and the farthest point from the two centroids is taken as the third point, By analogy, the results show that it can get higher accuracy, fewer iterations, and relatively good clustering effect.

Key words: non-IID(Independent and Identically Distributed), quadratic power coupling, Pearson correlation coefficient, clustering iteration;[K]-means clustering algorithm



关键词: 非独立同分布, 二次幂耦合, 皮尔森相关系数, 聚类迭代, [K]-means聚类算法