CHARACTERIZATION AND DETECTION OF NOISE IN CLUSTERING

被引：525

作者：

DAVE, RN

机构：

[1] Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark

来源：

PATTERN RECOGNITION LETTERS | 1991年 / 12卷 / 11期

关键词：

CLUSTERING; NOISE CLUSTER; CLASSIFICATION AMONGST NOISY DATA; K-MEANS ALGORITHMS; FUZZY K-MEANS ALGORITHMS;

D O I：

10.1016/0167-8655(91)90002-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A concept of 'Noise Cluster' is introduced such that noisy data points may be assigned to the noise class. The approach is developed for objective functional type (K-means or fuzzy K-means) algorithms, and its ability to detect 'good' clusters amongst noisy data is demonstrated. The approach presented is applicable to a variety of fuzzy clustering algorithms as well as regression analysis.

引用

页码：657 / 664

页数：8

共 9 条

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[2]

[Anonymous], 1988, ALGORITHMS CLUSTERIN

[3]

[Anonymous], 1981, PATTERN RECOGN

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[5]

Everitt B., 1974, CLUSTER ANAL

[6]

Gustafson E. E., 1979, P IEEE CDC SAN DIEG, P761

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PATTERN RECOGNITION, 1989, 22 (05) :603-607

[8]

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IEEE TRANSACTIONS ON COMPUTERS, 1971, C 20 (01) :68-&

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