Maximum certainty data partitioning

被引：51

作者：

Roberts, SJ ^{[1
]}

Everson, R ^{[1
]}

Rezek, I ^{[1
]}

机构：

[1] Univ London Imperial Coll Sci Technol & Med, Dept Elect & Elect Engn, Intelligent & Interact Syst Grp, London SW7 2BT, England

来源：

PATTERN RECOGNITION | 2000年 / 33卷 / 05期

关键词：

cluster analysis; data partitioning; information theory;

D O I：

10.1016/S0031-3203(99)00086-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Problems in data analysis often require the unsupervised partitioning of a dataset into clusters. Many methods exist for such partitioning but most have the weakness of being model-based (most assuming hyper-ellipsoidal clusters) or computationally infeasible in anything more than a three-dimensional data space. We re-consider the notion of cluster analysis in information-theoretic terms and show that minimisation of partition entropy can be used to estimate the number and structure of probable data generators. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

引用

页码：833 / 839

页数：7

共 10 条

[1] COMPARATIVE-ANALYSIS OF STATISTICAL PATTERN-RECOGNITION METHODS IN HIGH-DIMENSIONAL SETTINGS [J].