Collaborative Fuzzy Clustering From Multiple Weighted Views
Collaborative Fuzzy Clustering From Multiple Weighted Views
复制标题
来自多个加权视图的协作模糊聚类
DOI：
10.1109/tcyb.2014.2334595
复制
发表时间：
2015-04-01
影响因子：
11.8
通讯作者：
Qian, Pengjiang
中科院分区：
文献类型：
--
作者：
Jiang, Yizhang;Chung, Fu-Lai;Qian, Pengjiang
多视图数据聚类已成为数据挖掘、模式识别和机器学习领域的热门话题。为了实现有效的多视图聚类，必须解决两个问题，即如何将每个视图的聚类结果组合起来以及如何识别每个视图的重要性。本文在明确包含两个惩罚项的新目标函数的基础上，首次提出了一种基本的多视图模糊聚类算法——协同模糊c-均值（Co-FCM）。然后通过确定每个视图的重要性，将其扩展为加权视图版本，称为加权视图协同模糊c-均值（WV-Co-FCM）。WV-Co-FCM算法确实同时解决了上述两个问题。揭示了它与最新的多视图模糊聚类算法协同模糊k -均值（Co-FKM）的关系。在各种多视图数据集上的大量实验结果表明，所提出的WV-Co-FCM算法优于或至少与现有的最先进的多任务和多视图聚类算法相当，并且可以有效地识别数据集不同视图的重要性。
Clustering with multiview data is becoming a hot topic in data mining, pattern recognition, and machine learning. In order to realize an effective multiview clustering, two issues must be addressed, namely, how to combine the clustering result from each view and how to identify the importance of each view. In this paper, based on a newly proposed objective function which explicitly incorporates two penalty terms, a basic multiview fuzzy clustering algorithm, called collaborative fuzzy c-means (Co-FCM), is firstly proposed. It is then extended into its weighted view version, called weighted view collaborative fuzzy c-means (WV-Co-FCM), by identifying the importance of each view. The WV-Co-FCM algorithm indeed tackles the above two issues simultaneously. Its relationship with the latest multiview fuzzy clustering algorithm Collaborative Fuzzy K-Means (Co-FKM) is also revealed. Extensive experimental results on various multiview datasets indicate that the proposed WV-Co-FCM algorithm outperforms or is at least comparable to the existing state-of-the-art multitask and multiview clustering algorithms and the importance of different views of the datasets can be effectively identified.