Application of the least trimmed squares technique to prototype-based clustering

Jongwoo Kim; Raghu Krishnapuram; Rajesh Davé

doi:10.1016/0167-8655(96)00028-1

Application of the least trimmed squares technique to prototype-based clustering

Jongwoo Kim, Raghu Krishnapuram, Rajesh Davé

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

40 Scopus citations

Abstract

Prototype-based clustering algorithms such as the K-means and the Fuzzy C-Means algorithms are sensitive to noise and outliers. This paper shows how the Least Trimmed Squares technique can be incorporated into prototype-based clustering algorithms to make them robust.

Original language	English (US)
Pages (from-to)	633-641
Number of pages	9
Journal	Pattern Recognition Letters
Volume	17
Issue number	6
DOIs	https://doi.org/10.1016/0167-8655(96)00028-1
State	Published - May 15 1996

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

Keywords

Fuzzy C-means
K-means
Least trimmed squares
Noisy data
Robust clustering

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10.1016/0167-8655(96)00028-1

Cite this

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title = "Application of the least trimmed squares technique to prototype-based clustering",

abstract = "Prototype-based clustering algorithms such as the K-means and the Fuzzy C-Means algorithms are sensitive to noise and outliers. This paper shows how the Least Trimmed Squares technique can be incorporated into prototype-based clustering algorithms to make them robust.",

keywords = "Fuzzy C-means, K-means, Least trimmed squares, Noisy data, Robust clustering",

author = "Jongwoo Kim and Raghu Krishnapuram and Rajesh Dav{\'e}",

year = "1996",

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AU - Krishnapuram, Raghu

AU - Davé, Rajesh

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Y1 - 1996/5/15

N2 - Prototype-based clustering algorithms such as the K-means and the Fuzzy C-Means algorithms are sensitive to noise and outliers. This paper shows how the Least Trimmed Squares technique can be incorporated into prototype-based clustering algorithms to make them robust.

AB - Prototype-based clustering algorithms such as the K-means and the Fuzzy C-Means algorithms are sensitive to noise and outliers. This paper shows how the Least Trimmed Squares technique can be incorporated into prototype-based clustering algorithms to make them robust.

KW - Fuzzy C-means

KW - K-means

KW - Least trimmed squares

KW - Noisy data

KW - Robust clustering

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JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

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ER -

Application of the least trimmed squares technique to prototype-based clustering

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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