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View all- Wasilewski APrzyborowski M(2023)Clustering Methods for Adaptive e-Commerce User InterfacesRough Sets10.1007/978-3-031-50959-9_35(511-525)Online publication date: 5-Oct-2023
Gaussian Mixture Model Clustering with Incomplete Data
Authors: 
Yi Zhang, Miaomiao Li, Siwei Wang, Sisi Dai, Lei Luo, En Zhu, Huiying Xu, Xinzhong Zhu, Chaoyun Yao, Haoran ZhouAuthors Info & Claims
Yi Zhang, Miaomiao Li, Siwei Wang, Sisi Dai, Lei Luo, En Zhu, Huiying Xu, Xinzhong Zhu, Chaoyun Yao, Haoran ZhouAuthors Info & ClaimsArticle No.: 6, Pages 1 - 14
Abstract
Gaussian mixture model (GMM) clustering has been extensively studied due to its effectiveness and efficiency. Though demonstrating promising performance in various applications, it cannot effectively address the absent features among data, which is not uncommon in practical applications. In this article, different from existing approaches that first impute the absence and then perform GMM clustering tasks on the imputed data, we propose to integrate the imputation and GMM clustering into a unified learning procedure. Specifically, the missing data is filled by the result of GMM clustering, and the imputed data is then taken for GMM clustering. These two steps alternatively negotiate with each other to achieve optimum. By this way, the imputed data can best serve for GMM clustering. A two-step alternative algorithm with proved convergence is carefully designed to solve the resultant optimization problem. Extensive experiments have been conducted on eight UCI benchmark datasets, and the results have validated the effectiveness of the proposed algorithm.
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- Gaussian Mixture Model Clustering with Incomplete Data
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January 2021
353 pages
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Publication History
Published: 31 March 2021
Accepted: 01 June 2020
Revised: 01 May 2020
Received: 01 April 2020
Published in TOMM Volume 17, Issue 1s
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View all- Wasilewski APrzyborowski M(2023)Clustering Methods for Adaptive e-Commerce User InterfacesRough Sets10.1007/978-3-031-50959-9_35(511-525)Online publication date: 5-Oct-2023
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