ZHANG Nini1,2， GE Hongwei1,2
（1. School of Artificial Intelligence and Computer Science， Jiangnan University， Wuxi 214122， China； 2. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence， Jiangnan University, Wuxi 214122， China）
Abstract： The K-multiple-means (KMM) retains the simple and efficient advantages of the K-means algorithm by setting multiple subclasses, and improves its effect on non-convex data sets. And aiming at the problem that it cannot be applied to the Internet on a multi-view data set, a multi-view K-multiple-means (MKMM) clustering method is proposed in this paper. The new algorithm introduces view weight parameter, reserves the design of setting multiple subclasses， makes the number of clusters as constraint and obtains clusters by solving optimization problem. The new algorithm is compared with some popular multi-view clustering algorithms. The effectiveness of the new algorithm is proved through the analysis of the experimental results.
Key words： K-multiple-means (KMM) clustering; weight parameters; multi-view K-multiple-means (MKMM) method
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多视图K-多均值聚类算法
张倪妮1，2， 葛洪伟1,2
（1.江南大学 人工智能与计算机学院， 江苏 无锡 214122；2. 江南大学 江苏省模式识别与计算智能实验室，江苏 无锡 214122）
摘要:多均值聚类算法假设每个类拥有多个子类， 通过求解优化问题的方式来求解每个样本子类的划分和最终类簇的划分。 该算法弥补了K-均值算法在非球数据集上的劣势， 取得了较好的聚类效果， 但是该算法无法被运用到多视图数据集上。 本文提出了一种多视图K-多均值聚类算法， 保留了K-多均值设置多个子类的设计， 引入了视图权重参数， 将目标聚类数作为限制条件， 通过求解最优问题获得最终的类簇。 将本文提出的算法与流行的多视图聚类算法进行对比实验， 证明了本文算法的优越性。
关键词:K-多均值聚类； 权重参数； 多视图K-多均值算法
引用格式：ZHANG Nini， GE Hongwei. A multi-view K-multiple-means clustering method. Journal of Measurement Science and Instrumentation, 2021, 12（4）： 405-411. DOI： 10.3969／j.issn.1674-8042.2021.04.004
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