New information-based clustering method using Renyi's entropy and fuzzy C-means clustering

被引：0
|
作者：
Aghagolzadeh, M [1 ]
Soltanian-Zadeh, H [1 ]
Araabi, B [1 ]
机构：
[1] Univ Tehran, Dept Elect & Comp Engn, Tehran 14395, Iran
来源：
SEVENTH IASTED INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING | 2005年
关键词：
information theory; Renyi's entropy; top-down hierarchical algorithms; clustering;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a new clustering method based on Renyi entropy. The proposed method maximizes entropy of clusters using between and within clusters entropies. It is a top-down multi-resolution method and uses the initial clusters found by Fuzzy C-Means. Applications of the proposed algorithm on the synthetic data are compared with those of C-Means and Gustafson-Kessel algorithms. Results show superiority of the proposed algorithm to these methods.
引用
收藏
页码：410 / 413
页数：4
相关论文
共 50 条
  • [31] On Tolerant Fuzzy c-Means Clustering
    Hamasuna, Yukihiro
    Endo, Yasunori
    Miyamoto, Sadaaki
    JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS, 2009, 13 (04) : 421 - 428
  • [32] An Extended Fuzzy Local Information C-Means Clustering Algorithm
    Hou, Lili
    Zhang, Le
    Yang, Qiuying
    Wen, Ying
    2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2015,
  • [33] Fuzzy C-means clustering algorithm with deep neighborhood information
    Che H.
    Chen K.
    Wang Y.
    Liu X.
    Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2022, 50 (11): : 135 - 141
  • [34] Possibilistic C-Means Clustering Using Fuzzy Relations
    Zarandi, M. H. Fazel
    Kalhori, M. Rostam Niakan
    Jahromi, M. F.
    PROCEEDINGS OF THE 2013 JOINT IFSA WORLD CONGRESS AND NAFIPS ANNUAL MEETING (IFSA/NAFIPS), 2013, : 1137 - 1142
  • [35] Recommendation system using fuzzy C-means clustering
    Fang, KT
    Liu, CY
    INFORMATION TECHNOLOGY AND ORGANIZATIONS: TRENDS, ISSUES, CHALLENGES AND SOLUTIONS, VOLS 1 AND 2, 2003, : 137 - 139
  • [36] Random vector clustering using fuzzy c-means
    Hathaway, RJ
    Rogers, GW
    Bezdek, JC
    1998 CONFERENCE OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY - NAFIPS, 1998, : 251 - 255
  • [37] Generalised fuzzy local information C-means clustering algorithm
    Krinidis, S.
    Krinidis, M.
    ELECTRONICS LETTERS, 2012, 48 (23) : 1468 - 1469
  • [38] A COMPARATIVE STUDY OF FUZZY C-MEANS ALGORITHM AND ENTROPY-BASED FUZZY CLUSTERING ALGORITHMS
    Chattopadhyay, Subhagata
    Pratihar, Dilip Kumar
    De Sarkar, Sanjib Chandra
    COMPUTING AND INFORMATICS, 2011, 30 (04) : 701 - 720
  • [39] Generalised fuzzy c-means clustering algorithm with local information
    Memon, Kashif Hussain
    Lee, Dong-Ho
    IET IMAGE PROCESSING, 2017, 11 (01) : 1 - 12
  • [40] Mechanical Fault Diagnosis Method Based on LMD Shannon Entropy and Improved Fuzzy C-means Clustering
    Dong, Shaojiang
    Xu, Xiangyang
    Luo, Jiayuan
    INTERNATIONAL JOURNAL OF ACOUSTICS AND VIBRATION, 2017, 22 (02): : 211 - 217
12345