Soft clustering of multidimensional data: a semi-fuzzy approach

Shokri Z. Selim; M. A. Ismail

doi:10.1016/0031-3203(84)90054-2

Soft clustering of multidimensional data: a semi-fuzzy approach

Shokri Z. Selim, M. A. Ismail^*

^*Corresponding author for this work

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

65 Scopus citations

Abstract

This paper discusses new approaches to unsupervised fuzzy classification of multidimensional data. In the developed clustering models, patterns are considered to belong to some but not necessarily all clusters. Accordingly, such algorithms are called 'semi-fuzzy' or 'soft' clustering techniques. Several models to achieve this goal are investigated and corresponding implementation algorithms are developed. Experimental results are reported.

Original language	English
Pages (from-to)	559-568
Number of pages	10
Journal	Pattern Recognition
Volume	17
Issue number	5
DOIs	https://doi.org/10.1016/0031-3203(84)90054-2
State	Published - 1984
Externally published	Yes

Keywords

Fuzzy ISODATA algorithms
Fuzzy clustering models
Fuzzy pattern recognition
Fuzzy unsupervised learning
Semi-fuzzy classification
Soft clustering algorithms

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/0031-3203(84)90054-2

Cite this

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title = "Soft clustering of multidimensional data: a semi-fuzzy approach",

abstract = "This paper discusses new approaches to unsupervised fuzzy classification of multidimensional data. In the developed clustering models, patterns are considered to belong to some but not necessarily all clusters. Accordingly, such algorithms are called 'semi-fuzzy' or 'soft' clustering techniques. Several models to achieve this goal are investigated and corresponding implementation algorithms are developed. Experimental results are reported.",

keywords = "Fuzzy ISODATA algorithms, Fuzzy clustering models, Fuzzy pattern recognition, Fuzzy unsupervised learning, Semi-fuzzy classification, Soft clustering algorithms",

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year = "1984",

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T2 - a semi-fuzzy approach

AU - Selim, Shokri Z.

AU - Ismail, M. A.

PY - 1984

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AB - This paper discusses new approaches to unsupervised fuzzy classification of multidimensional data. In the developed clustering models, patterns are considered to belong to some but not necessarily all clusters. Accordingly, such algorithms are called 'semi-fuzzy' or 'soft' clustering techniques. Several models to achieve this goal are investigated and corresponding implementation algorithms are developed. Experimental results are reported.

KW - Fuzzy ISODATA algorithms

KW - Fuzzy clustering models

KW - Fuzzy pattern recognition

KW - Fuzzy unsupervised learning

KW - Semi-fuzzy classification

KW - Soft clustering algorithms

UR - https://www.scopus.com/pages/publications/0021644148

U2 - 10.1016/0031-3203(84)90054-2

DO - 10.1016/0031-3203(84)90054-2

M3 - Article

AN - SCOPUS:0021644148

SN - 0031-3203

VL - 17

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EP - 568

JO - Pattern Recognition

JF - Pattern Recognition

IS - 5

ER -

Soft clustering of multidimensional data: a semi-fuzzy approach

Abstract

Keywords

ASJC Scopus subject areas

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