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

来源：

PATTERN RECOGNITION LETTERS | 1994年 / 15卷 / 06期

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

页码：607 / 610

页数：4

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