A DETERMINISTIC ANNEALING APPROACH TO CLUSTERING

被引：242
|
作者：
ROSE, K
GUREWITZ, E
FOX, G
机构：
[1] Caltech Concurrent Computation Program, California Institute of Technology, Pasadena, CA 91125
来源：
PATTERN RECOGNITION LETTERS | 1990年 / 11卷 / 09期
关键词：
clustering; fuzzy clustering; Pattern classification; statistical mechanics;
D O I：
10.1016/0167-8655(90)90010-Y
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A deterministic annealing technique is proposed for the nonconvex optimization problem of clustering. Deterministic annealing is used in order to avoid local minima of the given cost function which trap traditional techniques. A set of temperature parametrized Gibbs probability density functions relate each data point to each cluster. An effective cost function is defined and minimized at each temperature. It is shown that as the temperature approaches zero, the algorithm becomes the basic ISODATA algorithm. The method is independent of the initial choice of cluster means. Simulation results are given and show how the method succeeds in avoiding local minima. © 1990.
引用
收藏
页码：589 / 594
页数：6
相关论文
共 50 条
  • [1] DETERMINISTIC AND SIMULATED ANNEALING APPROACH TO FUZZY C-MEANS CLUSTERING
    Yasuda, Makoto
    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2009, 5 (12B): : 4981 - 4991
  • [2] Pairwise data clustering by deterministic annealing
    Hofmann, T
    Buhmann, JM
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1997, 19 (01) : 1 - 14
  • [3] Clustering of atmospheric data by the deterministic annealing
    Ruzmaikin, Alexander
    Guillaume, Alexandre
    JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS, 2014, 120 : 121 - 131
  • [4] Parallel clustering algorithm by deterministic annealing
    Yang, Guangwen
    Shi, Shuming
    2003, Press of Tsinghua University (43):
  • [5] Online Deterministic Annealing for Classification and Clustering
    Mavridis, Christos N.
    Baras, John S.
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (10) : 7125 - 7134
  • [6] Optimizing an organized modularity measure for topographic graph clustering: A deterministic annealing approach
    Rossi, Fabrice
    Villa-Vialaneix, Nathalie
    NEUROCOMPUTING, 2010, 73 (7-9) : 1142 - 1163
  • [7] Deterministic Annealing for Clustering: Tutorial and Computational Aspects
    Parekh, Pratik M.
    Katselis, Dimitrios
    Beck, Carolyn L.
    Salapaka, Srinivasa M.
    2015 AMERICAN CONTROL CONFERENCE (ACC), 2015, : 2906 - 2911
  • [8] A robust deterministic annealing algorithm for data clustering
    Yang, XL
    Song, Q
    Liu, S
    Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5, 2005, : 1878 - 1882
  • [9] A robust deterministic annealing algorithm for data clustering
    Yang, Xu-Lei
    Song, Qing
    Wu, Yi-Lei
    DATA & KNOWLEDGE ENGINEERING, 2007, 62 (01) : 84 - 100
  • [10] Deterministic Annealing Approach to Fuzzy C-Means Clustering Based on Entropy Maximization
    Yasuda, Makoto
    ADVANCES IN FUZZY SYSTEMS, 2011, 2011
12345