Characterization and detection of noise in clustering

Rajesh N. Dave

doi:10.1016/0167-8655(91)90002-4

Characterization and detection of noise in clustering

Rajesh N. Dave

Research output: Contribution to journal › Article › peer-review

636 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	657-664
Number of pages	8
Journal	Pattern Recognition Letters
Volume	12
Issue number	11
DOIs	https://doi.org/10.1016/0167-8655(91)90002-4
State	Published - Nov 1991

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

Keywords

Clustering
K-means algorithms
classification amongst noisy data
fuzzy K-means algorithms
noise cluster

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10.1016/0167-8655(91)90002-4

Cite this

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title = "Characterization and detection of noise in clustering",

abstract = "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.",

keywords = "Clustering, K-means algorithms, classification amongst noisy data, fuzzy K-means algorithms, noise cluster",

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year = "1991",

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AB - 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.

KW - Clustering

KW - K-means algorithms

KW - classification amongst noisy data

KW - fuzzy K-means algorithms

KW - noise cluster

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JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

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ER -

Characterization and detection of noise in clustering

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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