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Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks

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Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks. / Alp Erilli, N.; Yolcu, Ufuk; Eǧrioǧlu, Erol et al.
In: Expert Systems with Applications, Vol. 38, No. 3, 01.03.2011, p. 2248-2252.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Alp Erilli, N, Yolcu, U, Eǧrioǧlu, E, Hakan Aladaǧ, Ĉ & Öner, Y 2011, 'Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks', Expert Systems with Applications, vol. 38, no. 3, pp. 2248-2252. https://doi.org/10.1016/j.eswa.2010.08.012

APA

Alp Erilli, N., Yolcu, U., Eǧrioǧlu, E., Hakan Aladaǧ, Ĉ., & Öner, Y. (2011). Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks. Expert Systems with Applications, 38(3), 2248-2252. https://doi.org/10.1016/j.eswa.2010.08.012

Vancouver

Alp Erilli N, Yolcu U, Eǧrioǧlu E, Hakan Aladaǧ Ĉ, Öner Y. Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks. Expert Systems with Applications. 2011 Mar 1;38(3):2248-2252. doi: 10.1016/j.eswa.2010.08.012

Author

Alp Erilli, N. ; Yolcu, Ufuk ; Eǧrioǧlu, Erol et al. / Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks. In: Expert Systems with Applications. 2011 ; Vol. 38, No. 3. pp. 2248-2252.

Bibtex

@article{39f1b5667a364193ab19b04609d0bd32,
title = "Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks",
abstract = "In a clustering problem, it would be better to use fuzzy clustering if there was an uncertainty in determining clusters or memberships of some units. Determining the number of cluster has an important role on obtaining sensible and sound results in clustering analysis. In many clustering algorithm, it is firstly need to know number of cluster. However, there is no pre-information about the number of cluster in general. The process of determining the most proper number of cluster is called as cluster validation. In the available fuzzy clustering literature, the most proper number of cluster is determined by utilizing cluster validation indices. When the data contain complexity are being analyzed, cluster validation indices can produce conflictive results. Also, there is no criterion point out the best index. In this study, artificial neural networks are employed to determine the number of cluster. The data is taken as input so the output is membership degree. The proposed method is applied some data and obtained results are compared to those obtained from validation indices like PC, XB, and CE. It is shown that the proposed method produce accurate results.",
keywords = "Artificial neural networks, Cluster validation index, Fuzzy clustering, Number of cluster",
author = "{Alp Erilli}, N. and Ufuk Yolcu and Erol Eǧrioǧlu and {Hakan Aladaǧ}, Ĉ and Y{\"u}ksel {\"O}ner",
year = "2011",
month = mar,
day = "1",
doi = "10.1016/j.eswa.2010.08.012",
language = "English",
volume = "38",
pages = "2248--2252",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Ltd",
number = "3",

}

RIS

TY - JOUR

T1 - Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks

AU - Alp Erilli, N.

AU - Yolcu, Ufuk

AU - Eǧrioǧlu, Erol

AU - Hakan Aladaǧ, Ĉ

AU - Öner, Yüksel

PY - 2011/3/1

Y1 - 2011/3/1

N2 - In a clustering problem, it would be better to use fuzzy clustering if there was an uncertainty in determining clusters or memberships of some units. Determining the number of cluster has an important role on obtaining sensible and sound results in clustering analysis. In many clustering algorithm, it is firstly need to know number of cluster. However, there is no pre-information about the number of cluster in general. The process of determining the most proper number of cluster is called as cluster validation. In the available fuzzy clustering literature, the most proper number of cluster is determined by utilizing cluster validation indices. When the data contain complexity are being analyzed, cluster validation indices can produce conflictive results. Also, there is no criterion point out the best index. In this study, artificial neural networks are employed to determine the number of cluster. The data is taken as input so the output is membership degree. The proposed method is applied some data and obtained results are compared to those obtained from validation indices like PC, XB, and CE. It is shown that the proposed method produce accurate results.

AB - In a clustering problem, it would be better to use fuzzy clustering if there was an uncertainty in determining clusters or memberships of some units. Determining the number of cluster has an important role on obtaining sensible and sound results in clustering analysis. In many clustering algorithm, it is firstly need to know number of cluster. However, there is no pre-information about the number of cluster in general. The process of determining the most proper number of cluster is called as cluster validation. In the available fuzzy clustering literature, the most proper number of cluster is determined by utilizing cluster validation indices. When the data contain complexity are being analyzed, cluster validation indices can produce conflictive results. Also, there is no criterion point out the best index. In this study, artificial neural networks are employed to determine the number of cluster. The data is taken as input so the output is membership degree. The proposed method is applied some data and obtained results are compared to those obtained from validation indices like PC, XB, and CE. It is shown that the proposed method produce accurate results.

KW - Artificial neural networks

KW - Cluster validation index

KW - Fuzzy clustering

KW - Number of cluster

U2 - 10.1016/j.eswa.2010.08.012

DO - 10.1016/j.eswa.2010.08.012

M3 - Journal article

AN - SCOPUS:78049529962

VL - 38

SP - 2248

EP - 2252

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 3

ER -