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Author:

Du, Yimin (Du, Yimin.) | Wu, Guixing (Wu, Guixing.) | Tang, Guolin (Tang, Guolin.)

Indexed by:

CPCI-S

Abstract:

Clustering plays an important role in data mining and machine learning. Then, intuitionistic fuzzy sets (IFSs) are flexible and practical in dealing with vagueness and uncertainty problems. To cluster the information expressed by intuitionistic fuzzy data, this paper proposes the joint training auto-encoder based intuitionistic fuzzy clustering algorithm. Firstly, we propose the auto-encoder based intuitionistic fuzzy clustering by utilizing similarity measure of IFSs, auto encoder and k-means algorithm. Then, we propose the joint training auto-encoder based intuitionistic fuzzy clustering algorithm by utilizing the proposed auto encoder based intuitionistic fuzzy clustering and two kinds of similarity measures for the clustering analysis of intuitionistic fuzzy data. Lastly, several experiments are provided to verify the effectiveness of the proposed intuitionistic fuzzy clustering algorithms.

Keyword:

intuitionistic fuzzy set similarity measure clustering auto-encoder

Author Community:

  • [ 1 ] [Du, Yimin]Univ Sci & Technol China, Sch Software Engn, Hefei, Anhui, Peoples R China
  • [ 2 ] [Wu, Guixing]Univ Sci & Technol China, Sch Software Engn, Hefei, Anhui, Peoples R China
  • [ 3 ] [Tang, Guolin]Beijing Univ Technol, Sch Econ & Management, Beijing, Peoples R China

Reprint Author's Address:

  • [Du, Yimin]Univ Sci & Technol China, Sch Software Engn, Hefei, Anhui, Peoples R China

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Source :

2017 12TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (IEEE ISKE)

Year: 2017

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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