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A multi-view ensemble clustering approach using joint affinity matrix.
- Source :
-
Expert Systems with Applications . Apr2023, Vol. 216, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
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. • 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MATRICES (Mathematics)
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 09574174
- Volume :
- 216
- Database :
- Academic Search Index
- Journal :
- Expert Systems with Applications
- Publication Type :
- Academic Journal
- Accession number :
- 161363133
- Full Text :
- https://doi.org/10.1016/j.eswa.2022.119484