A Novel Hybrid Clustering Method for Multi-Attribute Decision-Making Datasets

Kai Ping Liu; Wen Chin Chen; Ting Cheng Chang

doi:10.4028/www.scientific.net/AMR.482-484.103

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 482-484A Novel Hybrid Clustering Method for...

A Novel Hybrid Clustering Method for Multi-Attribute Decision-Making Datasets

Abstract:

A function is proposed for descritizing and classifying the uncertain data of multi-attribute decision-making (MADM) datasets using a hybrid scheme incorporating fuzzy set theory, Rough Set (RS) theory and a modified form of the PBMF index function. The proposed MADM index function is used to extend the applicability of the single-attribute decision-making (SADM) function. The validity of the proposed MADM index function is evaluated by comparing the descritizing results obtained for a simple hypothetical function with those obtained using a SADM function and the conventional PBMF function.

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Periodical:

Advanced Materials Research (Volumes 482-484)

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103-108

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.482-484.103

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Online since:

February 2012

Authors:

Kai Ping Liu, Wen Chin Chen, Ting Cheng Chang

Keywords:

Fuzzy C-Means (FCM), Fuzzy Set Theory, MADM Index Function, Rough Set Theory (RST)

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References

[1] Chen, Shyi-Ming, Lee, Li-Wei, Fuzzy multiple attributes group decision-making based on the ranking values and the arithmetic operations of interval type-2 fuzzy sets, Expert Systems with Applications 37 (2010), 824-833.

DOI: 10.1016/j.eswa.2009.06.094

Google Scholar

[2] Pakhira, Malay K., Bandyopadhyay, Sanghamitra, Maulik, Ujjwal,Validity index for crisp and fuzzy clusters, Pattern Recognition 37 (2004), 487-501.

DOI: 10.1016/j.patcog.2003.06.005

Google Scholar

[3] Wang, Weina, Zhang, Yunjie, On fuzzy cluster validity indices, Fuzzy Sets and Systems, 158 (2007), 2095-2117.

DOI: 10.1016/j.fss.2007.03.004

Google Scholar

[4] Huang, Kuang Yu, Applications of an Enhanced Cluster Validity Index method based on the Fuzzy C-means and Rough Set Theories to Partition and Classification, Expert Systems With Applications, 37(2010), 8757-8769.

DOI: 10.1016/j.eswa.2010.06.032

Google Scholar

[5] Huang, Kuang Yu, Jane, Chuen-Jiuan, A Hybrid Model for Stock Market Forecasting and Portfolio Selection Based on ARX, Grey System and RS Theories, Expert Systems With Applications, 36 (3 P1) (2009), 5387-5392

DOI: 10.1016/j.eswa.2008.06.103

Google Scholar

[6] Huang, Kuang Yu, Application of VPRS model with enhanced threshold parameter selection mechanism to automatic stock market forecasting and portfolio selection, Expert Systems With Applications, 36(9) (2009), 11652-11661.

DOI: 10.1016/j.eswa.2009.03.028

Google Scholar

[7] Glackina, Cornelius, Maguirea, Liam, McIvorb, Ronan, Humphreysb, Paul, Hermana, Pawel, A comparison of fuzzy strategies for corporate acquisition analysis, Fuzzy Sets and Systems, 158 (2007), 2039-2056.

DOI: 10.1016/j.fss.2007.03.020

Google Scholar

[8] Dunn, J.C., A fuzzy relative of the ISODATA process its use in detecting compact well-separated clusters, J. Cybernet. 3 (1974), 32-57.

DOI: 10.1080/01969727308546046

Google Scholar

[9] Zadeh, L.A., Fuzzy sets, Inf. Control, 8 (1965), 338-353.

Google Scholar