D-fuzzy clustering

Kaoru Hirota; Witold Pedrycz

doi:10.1016/0167-8655(94)00090-P

D-fuzzy clustering

Kaoru Hirota, Witold Pedrycz^*

^*Corresponding author for this work

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

11 Citations (Scopus)

Abstract

The proposed clustering algorithm is aimed at revealing the structure within the patterns under a simultaneous satisfaction of directionality constraints. These constraints are utilized to cope with functional relationships between the specified features of the patterns. To address this aspect of directionality, the introduced objective function (clustering criterion) is asymmetric. Proposed is also a criterion determining a "plausible" number of clusters within the data set. The instantaneous use of the outcomes of clustering for system identification will be also revealed. Numerical studies use synthetic and experimental data.

Original language	English
Pages (from-to)	193-200
Number of pages	8
Journal	Pattern Recognition Letters
Volume	16
Issue number	2
DOIs	https://doi.org/10.1016/0167-8655(94)00090-P
Publication status	Published - Feb 1995
Externally published	Yes

Keywords

Directionality constraints
Fuzzy clustering
Identification
Learning
Similarity of fuzzy sets

Access to Document

10.1016/0167-8655(94)00090-P

Cite this

Hirota, K., & Pedrycz, W. (1995). D-fuzzy clustering. Pattern Recognition Letters, 16(2), 193-200. https://doi.org/10.1016/0167-8655(94)00090-P

@article{3ac1fcf57e414376ad8df09a7e2f3ed6,

title = "D-fuzzy clustering",

abstract = "The proposed clustering algorithm is aimed at revealing the structure within the patterns under a simultaneous satisfaction of directionality constraints. These constraints are utilized to cope with functional relationships between the specified features of the patterns. To address this aspect of directionality, the introduced objective function (clustering criterion) is asymmetric. Proposed is also a criterion determining a {"}plausible{"} number of clusters within the data set. The instantaneous use of the outcomes of clustering for system identification will be also revealed. Numerical studies use synthetic and experimental data.",

keywords = "Directionality constraints, Fuzzy clustering, Identification, Learning, Similarity of fuzzy sets",

author = "Kaoru Hirota and Witold Pedrycz",

year = "1995",

month = feb,

doi = "10.1016/0167-8655(94)00090-P",

language = "English",

volume = "16",

pages = "193--200",

journal = "Pattern Recognition Letters",

issn = "0167-8655",

publisher = "Elsevier B.V.",

number = "2",

}

TY - JOUR

T1 - D-fuzzy clustering

AU - Hirota, Kaoru

AU - Pedrycz, Witold

PY - 1995/2

Y1 - 1995/2

N2 - The proposed clustering algorithm is aimed at revealing the structure within the patterns under a simultaneous satisfaction of directionality constraints. These constraints are utilized to cope with functional relationships between the specified features of the patterns. To address this aspect of directionality, the introduced objective function (clustering criterion) is asymmetric. Proposed is also a criterion determining a "plausible" number of clusters within the data set. The instantaneous use of the outcomes of clustering for system identification will be also revealed. Numerical studies use synthetic and experimental data.

AB - The proposed clustering algorithm is aimed at revealing the structure within the patterns under a simultaneous satisfaction of directionality constraints. These constraints are utilized to cope with functional relationships between the specified features of the patterns. To address this aspect of directionality, the introduced objective function (clustering criterion) is asymmetric. Proposed is also a criterion determining a "plausible" number of clusters within the data set. The instantaneous use of the outcomes of clustering for system identification will be also revealed. Numerical studies use synthetic and experimental data.

KW - Directionality constraints

KW - Fuzzy clustering

KW - Identification

KW - Learning

KW - Similarity of fuzzy sets

UR - http://www.scopus.com/inward/record.url?scp=0029250086&partnerID=8YFLogxK

U2 - 10.1016/0167-8655(94)00090-P

DO - 10.1016/0167-8655(94)00090-P

M3 - Article

AN - SCOPUS:0029250086

SN - 0167-8655

VL - 16

SP - 193

EP - 200

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

IS - 2

ER -

D-fuzzy clustering

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this