IMPROVED CLUSTERING USING DETERMINISTIC ANNEALING WITH A GRADIENT DESCENT TECHNIQUE

被引:3
|
作者
QIU, G [1 ]
VARLEY, MR [1 ]
TERRELL, TJ [1 ]
机构
[1] UNIV CENT LANCASHIRE,DEPT COMP & ELECTR,PRESTON PR1 2HE,ENGLAND
关键词
CLUSTERING; PATTERN CLASSIFICATION; GRADIENT DESCENT; STATISTICAL MECHANICS;
D O I
10.1016/0167-8655(94)90021-3
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Various techniques exist to solve the non-convex optimization problem of clustering. Recent developments have employed a deterministic annealing approach to solving this problem. In this letter a new approximation clustering algorithm, incorporating a gradient descent technique with deterministic annealing, is described. Results are presented for this new method, and its performance is compared with the K-means algorithm and a previously used deterministic annealing clustering algorithm. The new method is shown to produce more effective and robust clustering.
引用
收藏
页码:607 / 610
页数:4
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