A multi-view ensemble clustering approach using joint affinity matrix. (15th April 2023)

• Record Type: Journal Article
• Authors: Niu, Xueying; Zhang, Chaowei; Zhao, Xiaojie; Hu, Lihua; Zhang, Jifu
• Is Part Of: Expert systems with applications. Volume 216(2023)
• Publication Date: 2023-04-15
• Subjects: Multi-view ensemble clustering -- Joint affinity matrix -- Core data object -- Sample-level weight -- Influence index; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.119484
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: In multi-view ensemble clustering, the correctly-partitioned data objects should be assigned with a higher weight, thereby helping to decrease the influence of incorrectly-partitioned data objects. Therefore, different data objects should be treated separately instead of being set the same view weight as traditional solutions. In this paper, a multi-view ensemble clustering approach is proposed using joint affinity matrix, which is generated by sample-level weight. Firstly, a new concept of core data objects is defined according to the influence index and Gaussian Mixed Model, and basic partitions and sample-level weights can be yielded for every view. Secondly, a joint affinity matrix, which maintains pairwise similarities of all data objects, is generated using the sample-level weights. Consequently, data objects can be effectively assigned to the correct partition. Thirdly, a multi-view ensemble clustering algorithm is proposed using the joint affinity matrix. In the end, experimental results on benchmark datasets validate the efficacy of the algorithm with state-of-the-art baselines. Highlights: Core data objects are redefined using Gaussian Mixed Model and nearest neighbors. A single view clustering method without the specified number of clusters is proposed. A new fusion mechanism is proposed to generate joint affinity matrix. A multi-view ensemble clustering algorithm is proposed using joint affinity matrix.