The existing multi-view clustering algorithm can make full use of multi-view information to cluster, so its effect is better than that of the single view clustering algorithm. However, in the clustering process, most of the multi-view clustering algorithms assign the same weight values for each view, which will seriously affect the final result of clustering. The current weighted K-means clustering algorithm can solve the above problem of weight assigning for the multi-view clustering tasks, but its weight will be divided by zero in the iteration process, which leads to the loss of related perspectives. For this, we propose an optimizing weighted multi-view K-means clustering algorithm (MKSC) which assigns weight for each view and uses the weighted strategy to effectively determine the importance of the various perspectives, optimizing the weight of each view by introducing a constant and with K-means to cluster. The algorithm is verified by experiments based on artificial data set and real dataset, results of which have shown that it has better clustering performance than the existing multi view clustering technology.%现存的多视角聚类算法能够充分利用多个视角的信息进行聚类,因而其聚类效果较单视角聚类算法更优,但是绝大多数多视角聚类算法在聚类过程中为各个视角赋予了同等的权重值,这对于划分不明确的视角,会严重影响聚类的最终结果.目前的加权K-means聚类算法在面对多视角聚类任务时,能解决上述权重的取值分配问题,但其权重在迭代过程中会出现除以零错误,造成相关视角的丢失.针对这个问题,提出了一种优化加权多视角K-means聚类算法(MKSC).该算法给每个视角分配权重,利用加权策略有效地控制各个视角的重要程度,通过引入常数对每个视角的权重进行优化,使用K-means进行聚类.通过基于人工数据集和真实数据集的实验对该算法进行验证,实验结果表明该算法较已有的多视角聚类技术具有更好的聚类性能.
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