Fuzzy Entropy Based Fuzzy c-Means Clustering with Deterministic and Simulated Annealing Methods

被引：8

作者：

Yasuda, Makoto ^{[1
]}

Furuhashi, Takeshi ^{[2
]}

机构：

[1] Gifu Natl Coll Technol, Dept Elect & Comp Engn, Motosu 5010495, Japan

[2] Nagoya Univ, Dept Computat Sci & Engn, Nagoya, Aichi 4648603, Japan

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2009年 / E92D卷 / 06期

关键词：

fuzzy c-means clustering; fuzzy entropy; Fermi-Dirac distribution; deterministic annealing; simulated annealing; OPTIMIZATION;

D O I：

10.1587/transinf.E92.D.1232

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article explains how to apply the deterministic annealing (DA) and simulated annealing (SA) methods to fuzzy entropy based fuzzy c-means clustering. By regularizing the fuzzy c-means method with fuzzy entropy, a membership function similar to the Fermi-Dirac distribution function, well known in statistical mechanics, is obtained, and, while optimizing its parameters by SA, the minimum of the Helmholtz free energy for fuzzy c-means clustering is searched by DA. Numerical experiments are performed and the obtained results indicate that this combinatorial algorithm of SA and DA can represent various cluster shapes and divide data more properly and stably than the standard single DA algorithm.

引用

页码：1232 / 1239

页数：8

共 50 条

[21] A Graph-Based Watershed Merging using Fuzzy C-Means and Simulated Annealing for Image Segmentation [J].

Vadiveloo, Mogana ;

Abdullah, Rosni ;

Rajeswari, Mandava .

SEVENTH INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2015), 2015, 9817

[22] Developing the fuzzy c-means clustering algorithm based on maximum entropy for multitarget tracking in a cluttered environment [J].

Chen, Xiao ;

Li, Yaan ;

Yu, Jing ;

Li, Yuxing .

JOURNAL OF APPLIED REMOTE SENSING, 2018, 12

[23] Image segmentation by multi-level thresholding based on C-means clustering algorithms and fuzzy entropy [J].

Zhao, Feng ;

Fan, Jiulun .

FUZZY INFORMATION AND ENGINEERING, PROCEEDINGS, 2007, 40 :113-+

[24] Unconstrained Fuzzy C-Means Based on Entropy Regularization: An Equivalent Model [J].

Nie, Feiping ;

Zhang, Runxin ;

Duan, Yu ;

Wang, Rong .

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2025, 37 (02) :979-990

[25] A review on advanced c-means clustering models based on fuzzy logic [J].

Naghi, Mirtill-Boglarka ;

Kovacs, Levente ;

Szilagyi, Laszlo .

2023 IEEE 21ST WORLD SYMPOSIUM ON APPLIED MACHINE INTELLIGENCE AND INFORMATICS, SAMI, 2023, :293-298

[26] A novel quadrature particle filtering based on fuzzy c-means clustering [J].

Li Liang-qun ;

Xie Wei-xin ;

Liu Zong-xiang .

KNOWLEDGE-BASED SYSTEMS, 2016, 106 :105-115

[27] Drilling Wear Recognition based on Fuzzy C-means Clustering Algorithm [J].

Yan, Mingxia .

MATERIALS PROCESSING TECHNOLOGY II, PTS 1-4, 2012, 538-541 :1408-1412

[28] Weighted fuzzy time series forecasting based on improved fuzzy C-means clustering algorithm [J].

Sang, Xiaoshuang ;

Zhao, Qinghua ;

Lu, Hong ;

Lu, Jianfeng .

PROCEEDINGS OF THE 2018 IEEE INTERNATIONAL CONFERENCE ON PROGRESS IN INFORMATICS AND COMPUTING (PIC), 2018, :80-84

[29] Fuzzy c-means clustering with prior biological knowledge [J].

Tari, Luis ;

Baral, Chitta ;

Kim, Seungchan .

JOURNAL OF BIOMEDICAL INFORMATICS, 2009, 42 (01) :74-81

[30] Vehicle Tracking Based on Fuzzy C-Means Clustering and Grey Model [J].

Yang Donghe .

MATERIAL SCIENCE, CIVIL ENGINEERING AND ARCHITECTURE SCIENCE, MECHANICAL ENGINEERING AND MANUFACTURING TECHNOLOGY II, 2014, 651-653 :551-554

← 1 2 3 4 5 →