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2016 | 7 | nr 2 | 29--33
Tytuł artykułu

Application of Expectation Maximization Method for Purchase Decision-Making Support in Welding Branch

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article presents a study of applying the proposed method of cluster analysis to support purchasing decisions in the welding industry. The authors analyze the usefulness of the non-hierarchical method, Expectation Maximization (EM), in the selection of material (212 combinations of flux and wire melt) for the SAW (Submerged Arc Welding) method process. The proposed approach to cluster analysis is proved as useful in supporting purchase decisions. (original abstract)
Rocznik
Tom
7
Numer
Strony
29--33
Opis fizyczny
Twórcy
  • Poznan University of Technology
  • Poznan University of Technology
  • Poznan University of Technology
Bibliografia
  • [1] Chai J., Liu J., Ngai E., Application of decision making techniques in supplier selection: A systematic review of literature, Exp. Sys. with Appl., 40, 10, 3872-3885, 2013.
  • [2] Rogalewicz M., Kujawińska A., Piłacińska M., Selection of data mining method for multidimensional evaluation of the manufacturing process state, Man. and Prod. Eng. Review, 3, 2, 27-35, 2012.
  • [3] Cao L., Domain-driven Data Mining: challenges and prospects, Know. and Data Eng., IEEE Trans., 22, 6, 755-769, 2010.
  • [4] Jancikova Z., Roubićek V., Juchelkova D., Application of artificial intelligence methods for prediction of steel mechanical properties, Metalurgija, 47, 4, 339-342, 2008.
  • [5] Grudzień Ł., Hamrol A., Information quality in design process documentation of quality management systems, Int. J. of Inf. Man., 36, 4, 599-606, 2016.
  • [6] Popat S., Emmanuel M., Review and comparative study of clustering techniques, Int. J. of Comp. Sc. and Inf. Tech., 5, 1, 805-812, 2014.
  • [7] Stachowiak A., Żywica P., Dyczkowski K., Wójtowicz A., An Interval-Valued Fuzzy ClassLer Based on an Uncertainty- Aware Similarity Measure, Intelligent Systems'2014, Advances in Intelligent Systems and Computing, Springer, 32, 741-751, 2015.
  • [8] Murtagh F., Contreras P., Algorithms for hierarchical clustering: an overview, Wiley Int. Rev.: Data Mining and Know. Disc., 2, 1, 86-97, 2012.
  • [9] Jain K., Murty M., Flynn P., Data clustering: a review, ACM Comp. Surv. (CSUR), 31, 3, 264-323, 1999.
  • [10] Abbas A., Comparisons between data clustering algorithms, Int. Arab. J. of Inf. Tech., 5, 1, 320-325, 2008.
  • [11] Sika R., Ignaszak Z., Implementation of the KMES Quality system for data acquisition and processing on the example of chosen foundry, Arch. of Foundry, 8, 3, 97-102, 2008.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171439340

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