Fuzzy Style K-Plane Clustering

Gu, Suhang1; Nojima, Yusuke2; Ishibuchi, Hisao3; Wang, Shitong1,4

2021-06-01

10.1109/TFUZZ.2020.2979676

IEEE TRANSACTIONS ON FUZZY SYSTEMS   影响因子和分区

1063-6706

1941-0034

As the first attempt, this article considers how to provide a design methodology for style clustering on stylistic data, where each cluster depends on both the similarities between data samples and its latently or apparently distinguishable style. By taking our previous fuzzy k plane clustering algorithm as the basic framework, a fuzzy style k-plane clustering (S-KPC) algorithm is proposed to have its distinctive merits: First, the nuances between styles of clusters can be well identified by using the proposed twofold data representation. That is to say, style matrices are used to express the structure, hence style information of each cluster, whereas the augmentation of the original features of data with enhanced nodes is taken as an abstract representation so as to move the manifold structure of data apart. Such a twofold data representation can make us realize S-KPC readily in an incremental way. Second, by means of alternating optimization strategy, the objective function of S-KPC can be optimized such that each discriminant function of each cluster shares the advantages of both simple regression models and functional-link neural networks. Extensive experiments on synthetic and real-world datasets demonstrate that S-KPC has comparable clustering performance with several compared methods on the adopted ordinary datasets, and yet it obviously outperforms them on stylistic datasets.

Alternating optimization feature augmentation fuzzy k plane clustering style clustering stylistic data

SCI ; EI

英语

其他

National Natural Science Foundation of China[61572236,61772198,61972181] ; NSFC-JSPS[61711540041] ; Natural Science Foundation of Jiangsu Province[BK20191331] ; National First-Class Discipline Program of Light Industry and Engineering["LITE2018","CJ20190016"]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic

WOS:000658338600017

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20212310477120

Regression analysis

Information Sources and Analysis:903.1 ; Mathematical Statistics:922.2

ENGINEERING

Web of Science

1.Jiangnan Univ, Sch Digital Media, Wuxi 214122, Jiangsu, Peoples R China
2.Osaka Prefecture Univ, Dept Comp Sci, Osaka 5998531, Japan
3.South Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
4.Software Technol Jiangsu, Key Lab Media Design, Wuxi 214122, Jiangsu, Peoples R China

Gu, Suhang,Nojima, Yusuke,Ishibuchi, Hisao,et al. Fuzzy Style K-Plane Clustering[J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS,2021,29(6):1518-1532.

Gu, Suhang,Nojima, Yusuke,Ishibuchi, Hisao,&Wang, Shitong.(2021).Fuzzy Style K-Plane Clustering.IEEE TRANSACTIONS ON FUZZY SYSTEMS,29(6),1518-1532.

Gu, Suhang,et al."Fuzzy Style K-Plane Clustering".IEEE TRANSACTIONS ON FUZZY SYSTEMS 29.6(2021):1518-1532.