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Nonnegative Latent Factor Analysis-Incorporated and Feature-Weighted Fuzzy Double $c$-Means Clustering for Incomplete Data
Song, Yan1; Li, Ming1; Zhu, Zhengyu1; Yang, Guisong1; Luo, Xin2,3
2022-10-01
摘要Fuzzy c-means (FCM) clustering is a promising method to handle uncertainties in data clustering. However, the traditional FCM and most of its variants cannot address incomplete inputs. To this aim, a novel fuzzy clustering framework is put forward to perform highly accurate clustering on incomplete data. It adopts twofold ideas: 1) Utilizing a nonnegative latent factor model to prefill the missing data in the inputs by rigidly extracting involved entities' latent features, where the principle of a minibatch gradient descent algorithm is incorporated into a single latent factor-dependent, nonnegative and multiplicative update algorithm to accelerate the convergence rate; and 2) integrating the distribution of inputs and the weights of local features into the objective function through sparse self-representation and weighting allocation to focus on crucial features. In this way, a NLF analysis-incorporated and feature-weighted fuzzy double c-means clustering ((NFD)-D-2) method is achieved, where the data distribution and instance correlation are simultaneously considered with care. Experiments on 12 real-world datasets including both data and images with different missing rates show that the proposed (NFD)-D-2 method has a significant superiority over state-of-the-art fuzzy clustering methods.
关键词Big data clustering fuzzy double c-means incomplete data latent factor analysis local feature weights.
DOI10.1109/TFUZZ.2022.3144489
发表期刊IEEE TRANSACTIONS ON FUZZY SYSTEMS
ISSN1063-6706
卷号30期号:10页码:4165-4176
通讯作者Luo, Xin(lu-oxin21@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:000864186200015
语种英语