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April  2012, 8(2): 285-297. doi: 10.3934/jimo.2012.8.285

A decision making process application for the slurry production in ceramics via fuzzy cluster and data mining

1. 

Department of Industrial Engineering, University of Erciyes, Kayseri 38039, Turkey

2. 

Center for Applied Optimization Department of Industrial and Systems Engineering, University of Florida, 32611, United States

Received  December 2010 Revised  July 2011 Published  April 2012

To increase productivity, companies are in search of techniques that enable them to make faster and more effective decisions. Data mining and fuzzy clustering algorithms can serve for this purpose. This paper models the decision making process of a ceramics production company using a fuzzy clustering algorithm and data mining. Factors that affect the quality of slurry are measured over time. Using this data, a fuzzy clustering algorithm assigns the degrees of memberships of the slurry for the different quality clusters. An expert can decide on acceptance or rejection of slurry based on calculated degrees of memberships. In addition, by using data mining techniques we generated some rules that provide the optimum conditions for acceptance of the slurry.
Citation: Feyza Gürbüz, Panos M. Pardalos. A decision making process application for the slurry production in ceramics via fuzzy cluster and data mining. Journal of Industrial & Management Optimization, 2012, 8 (2) : 285-297. doi: 10.3934/jimo.2012.8.285
References:
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Adem Göleç, "Fuzzy Modelling Approach in Feature-Based Computer-Aided Process Planning,", Ph.D thesis, (2001). Google Scholar

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D. Hand, H. Manila and P. Smyth, "Principles of Data Mining,", A Bradford Book, (2001). Google Scholar

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Maurice Houtsma and Arun Swami, Set-oriented data mining in relational databases,, Data & Knowledge Engineering, 17 (1995), 245. doi: 10.1016/0169-023X(95)00024-M. Google Scholar

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K. Josien and T. W. Liao, Integrated use of fuzzy C-means and fuzzy KNN for GT part family and machine cell formation,, International Journal of Production Research, 38 (2000), 3513. doi: 10.1080/002075400422770. Google Scholar

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Han Jiawei and Micheline Kamber, "Data Mining: Concepts And Techniques,", University Of Simon Fraser, (2001). Google Scholar

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Andrew Kusiak and Mathew Smith, Data mining in design of products and production systems,, Annual Reviews in Control, 31 (2007), 147. doi: 10.1016/j.arcontrol.2007.03.003. Google Scholar

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Surendra Kumar and C. S. P. Rao, Application of ant colony, genetic algorithm and data mining-based techniques for scheduling,, Robotics and Computer-Integrated Manufacturing, 25 (2009), 901. doi: 10.1016/j.rcim.2009.04.015. Google Scholar

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Der-Chang Li and Chun-Wu Yeh, A non-parametric learning algorithm for small manufacturing data sets,, Expert Systems with Applications, 34 (2008), 391. doi: 10.1016/j.eswa.2006.09.008. Google Scholar

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Emmanouil Magkos, Manolis Maragoudakis, Vassilis Chrissikopoulos and Stefanos Gritzalis, Accurate and large-scale privacy-preserving data mining using the election paradigm,, Data & Knowledge Engineering, 68 (2009), 1224. doi: 10.1016/j.datak.2009.06.003. Google Scholar

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Atakan Öztürk, Sinan Kayalıgil and Nur E. Özdemirel, Manufacturing lead time estimation using data mining,, European Journal of Operational Research, 173 (2006), 683. doi: 10.1016/j.ejor.2005.03.015. Google Scholar

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Nikhil R. Pal and James C. Bezdek, On cluster validity for the fuzzy c-means model,, IEEE Transactions on Fuzzy Systems, 3 (1995), 370. doi: 10.1109/91.413225. Google Scholar

[23]

Lee Sangjae, Using data envelopment analysis and decision trees for efficiency analysis and recommendation of B2C controls,, Decision Support Systems, 49 (2010), 486. doi: 10.1016/j.dss.2010.06.002. Google Scholar

[24]

Magne Setnes, Supervised fuzzy clustering for rule extraction,, IEEE Transactions on Fuzzy Systems, 8 (2000), 416. doi: 10.1109/91.868948. Google Scholar

[25]

Michio Sugeno and Takahiro Yasukawa, A fuzzy-logic based approach to qualitative modeling,, IEEE Transactions on Fuzzy Systems, 1 (1993), 7. doi: 10.1109/TFUZZ.1993.390281. Google Scholar

[26]

, "User Manual of Polyanalyst 5,", 2005., (). Google Scholar

[27]

Wei Yan, Chun-Hsien Chen and Meng-Dar Shieh, Product concept generation and selection using sorting technique and fuzzy c-means algorithm,, Computers & Industrial Engineering, 50 (2006), 273. doi: 10.1016/j.cie.2006.05.003. Google Scholar

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Ian H. Witten and Eibe Frank, "Data Mining: Practical Machine Learning Tools and Techniques,", Elsevier, (2005). Google Scholar

show all references

References:
[1]

M. Arenas, A. Bilboa, M. V. Rodriguez Uria and M. Jimenez, A Fuzzy goal programming model for evaluating a hospital service performance,, in, (2001), 19. Google Scholar

[2]

Roelof K. Brouwer and Albert Groenwold, Modified fuzzy c-means for ordinal valued attributes with particle swarm for optimization,, Fuzzy Sets and Systems, 161 (2010), 1774. doi: 10.1016/j.fss.2009.10.019. Google Scholar

[3]

Toly Chen, Incorporating fuzzy c-means and a back-propagation network ensemble to job completion time prediction in a semiconductor fabrication factory,, Fuzzy Sets and Systems, 158 (2007), 2153. doi: 10.1016/j.fss.2007.04.013. Google Scholar

[4]

Wei-Chou Chen, Shian-Shyong Tseng and Ching-Yao Wang, Novel manufacturing defect detection method using association rule mining techniques,, Expert Systems with Applications, 29 (2005), 807. doi: 10.1016/j.eswa.2005.06.004. Google Scholar

[5]

Russ M. Dabbas and Hung-Nan Chen, Mining semiconductor manufacturing data for productivity improvement-An integrated relational database approach,, Computers in Industry, 45 (2001), 29. doi: 10.1016/S0166-3615(01)00079-3. Google Scholar

[6]

Margaret H. Dunham, "Data Mining: Introductory and Advanced Topics,", Prentice Hall PTR, (2002). Google Scholar

[7]

Mohammad R. Emami, Burhan I. Türksen and Andrew A. Goldenberg, An improved fuzzy modeling algorithm part I: Inference mechanism,, in, (1996). Google Scholar

[8]

Mohammad R. Emami, Burhan I. Türksen and Andrew A. Goldenberg, Development of a systematic methodology of fuzzy logic modeling,, IEEE Transactions on Fuzzy Systems, 6 (1998), 346. doi: 10.1109/91.705501. Google Scholar

[9]

Alex A. Freitas, "Data Mining and Knowledge Discovery with Evolutionary Algorithms,", Springer-Verlag, (2002). Google Scholar

[10]

J. L. Garcia-Lapresta, M. Martinez-Panero and L. L. Lazzari, A group decision making method using fuzzy triangular numbers,, in, (2001), 35. Google Scholar

[11]

Adem Göleç, "Fuzzy Modelling Approach in Feature-Based Computer-Aided Process Planning,", Ph.D thesis, (2001). Google Scholar

[12]

D. Hand, H. Manila and P. Smyth, "Principles of Data Mining,", A Bradford Book, (2001). Google Scholar

[13]

Maurice Houtsma and Arun Swami, Set-oriented data mining in relational databases,, Data & Knowledge Engineering, 17 (1995), 245. doi: 10.1016/0169-023X(95)00024-M. Google Scholar

[14]

K. Josien and T. W. Liao, Integrated use of fuzzy C-means and fuzzy KNN for GT part family and machine cell formation,, International Journal of Production Research, 38 (2000), 3513. doi: 10.1080/002075400422770. Google Scholar

[15]

Han Jiawei and Micheline Kamber, "Data Mining: Concepts And Techniques,", University Of Simon Fraser, (2001). Google Scholar

[16]

Andrew Kusiak and Mathew Smith, Data mining in design of products and production systems,, Annual Reviews in Control, 31 (2007), 147. doi: 10.1016/j.arcontrol.2007.03.003. Google Scholar

[17]

Surendra Kumar and C. S. P. Rao, Application of ant colony, genetic algorithm and data mining-based techniques for scheduling,, Robotics and Computer-Integrated Manufacturing, 25 (2009), 901. doi: 10.1016/j.rcim.2009.04.015. Google Scholar

[18]

Der-Chang Li and Chun-Wu Yeh, A non-parametric learning algorithm for small manufacturing data sets,, Expert Systems with Applications, 34 (2008), 391. doi: 10.1016/j.eswa.2006.09.008. Google Scholar

[19]

Emmanouil Magkos, Manolis Maragoudakis, Vassilis Chrissikopoulos and Stefanos Gritzalis, Accurate and large-scale privacy-preserving data mining using the election paradigm,, Data & Knowledge Engineering, 68 (2009), 1224. doi: 10.1016/j.datak.2009.06.003. Google Scholar

[20]

Ye Nong, "The Handbook of Data Mining,", Lawrence Erlbaum Associates Publishers, (2003). Google Scholar

[21]

Atakan Öztürk, Sinan Kayalıgil and Nur E. Özdemirel, Manufacturing lead time estimation using data mining,, European Journal of Operational Research, 173 (2006), 683. doi: 10.1016/j.ejor.2005.03.015. Google Scholar

[22]

Nikhil R. Pal and James C. Bezdek, On cluster validity for the fuzzy c-means model,, IEEE Transactions on Fuzzy Systems, 3 (1995), 370. doi: 10.1109/91.413225. Google Scholar

[23]

Lee Sangjae, Using data envelopment analysis and decision trees for efficiency analysis and recommendation of B2C controls,, Decision Support Systems, 49 (2010), 486. doi: 10.1016/j.dss.2010.06.002. Google Scholar

[24]

Magne Setnes, Supervised fuzzy clustering for rule extraction,, IEEE Transactions on Fuzzy Systems, 8 (2000), 416. doi: 10.1109/91.868948. Google Scholar

[25]

Michio Sugeno and Takahiro Yasukawa, A fuzzy-logic based approach to qualitative modeling,, IEEE Transactions on Fuzzy Systems, 1 (1993), 7. doi: 10.1109/TFUZZ.1993.390281. Google Scholar

[26]

, "User Manual of Polyanalyst 5,", 2005., (). Google Scholar

[27]

Wei Yan, Chun-Hsien Chen and Meng-Dar Shieh, Product concept generation and selection using sorting technique and fuzzy c-means algorithm,, Computers & Industrial Engineering, 50 (2006), 273. doi: 10.1016/j.cie.2006.05.003. Google Scholar

[28]

Ian H. Witten and Eibe Frank, "Data Mining: Practical Machine Learning Tools and Techniques,", Elsevier, (2005). Google Scholar

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