Clustering Ensemble Model Based on Self-Organizing Map Network
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-25
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
This paper proposes a clustering ensemble method that introduces cascade structure into the self-organizing map (SOM) to solve the problem of the poor performance of a single clusterer.
Cascaded SOM is an extension of classical SOM combined with the cascaded structure.
The method combines the outputs of multiple SOM networks in a cascaded manner using them as an input to another SOM network.
It also utilizes the characteristic of high-dimensional data insensitivity to changes in the values of a small number of dimensions to achieve the effect of ignoring part of the SOM network error output.
Since the initial parameters of the SOM network and the sample training order are randomly generated, the model does not need to provide different training samples for each SOM network to generate a differentiated SOM clusterer.
After testing on several classical datasets, the experimental results show that the model can effectively improve the accuracy of pattern recognition by 4%∼10%.
American Psychological Association (APA)
Hua, Wenqi& Mo, Lingfei. 2020. Clustering Ensemble Model Based on Self-Organizing Map Network. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1138736
Modern Language Association (MLA)
Hua, Wenqi& Mo, Lingfei. Clustering Ensemble Model Based on Self-Organizing Map Network. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1138736
American Medical Association (AMA)
Hua, Wenqi& Mo, Lingfei. Clustering Ensemble Model Based on Self-Organizing Map Network. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1138736
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138736