Analytical Review of the Applications of Multi-Criteria Decision Making in Data Mining

In recent years, multi-criteria decision making (MCDM) is a significant part of operations research (OR) and has become an interesting topic to researcher who works in the data mining (DM) field. The aim of this chapter is to provide an in-depth presentation of the contribution of MCDM in the field of DM. In order to develop a reliable knowledge base on accumulating knowledge from previous studies, we present a review of the usage of MCDM methods in DM field. The chapter presents methodology and application. The result shows that the most usage of MCDM in DM consists of evaluating classification algorithms, weighting criteria, and ranking association rules and clusters. Finally, some future research directions are suggested at the end of chapter.

[1]  Hülya Güçdemir,et al.  Integrating multi-criteria decision making and clustering for business customer segmentation , 2015, Ind. Manag. Data Syst..

[2]  Basilis Boutsinas,et al.  A method for improving the accuracy of data mining classification algorithms , 2009, Comput. Oper. Res..

[3]  Edmundas Kazimieras Zavadskas,et al.  Synergies of Data Mining and Multiple Attribute Decision Making , 2014 .

[4]  Soung Hie Kim,et al.  Prioritization of association rules in data mining: Multiple criteria decision approach , 2005, Expert Syst. Appl..

[5]  Shahrul Kamaruddin,et al.  Comparison of Multi Criteria Decision Making Methods From The Maintenance Alternative Selection Perspective , 2013 .

[6]  Marian B. Gorzalczany,et al.  Interpretable and accurate medical data classification - a multi-objective genetic-fuzzy optimization approach , 2017, Expert Syst. Appl..

[7]  Alessio Ishizaka,et al.  Review of the main developments in the analytic hierarchy process , 2011, Expert Syst. Appl..

[8]  E. Reissner On asymptotic solutions for nonsymmetric deformations of shallow shells of revolution , 1964 .

[9]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[10]  Zhiqiang Geng,et al.  Early warning modeling and analysis based on analytic hierarchy process integrated extreme learning machine (AHP-ELM): Application to food safety , 2017 .

[11]  Rahat Iqbal,et al.  Big data analytics: Computational intelligence techniques and application areas , 2020, Technological Forecasting and Social Change.

[12]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[13]  Yong Zhang,et al.  An incident information management framework based on data integration, data mining, and multi-criteria decision making , 2011, Decis. Support Syst..

[14]  Honggang Wang,et al.  User preferences based software defect detection algorithms selection using MCDM , 2012, Inf. Sci..

[15]  Gwo-Hshiung Tzeng,et al.  Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS , 2004, Eur. J. Oper. Res..

[16]  Sungyoung Lee,et al.  Accurate multi-criteria decision making methodology for recommending machine learning algorithm , 2017, Expert Syst. Appl..

[17]  Rahat Iqbal,et al.  TEMPORARY REMOVAL: Big Data analytics: Computational intelligence techniques and application areas , 2016 .

[18]  T. L. Saaty A Scaling Method for Priorities in Hierarchical Structures , 1977 .

[19]  Jacky Akoka,et al.  Research on Big Data - A systematic mapping study , 2017, Comput. Stand. Interfaces.

[20]  Mandana Rezaeiahari,et al.  AHP based Classification Algorithm Selection for Clinical Decision Support System Development , 2014, Complex Adaptive Systems.

[21]  Duen-Ren Liu,et al.  Integrating AHP and data mining for product recommendation based on customer lifetime value , 2005, Inf. Manag..

[22]  Kamran Rezaie,et al.  Evaluating performance of Iranian cement firms using an integrated fuzzy AHP–VIKOR method , 2014 .

[23]  Muhammad Badruddin Khan,et al.  Machine Learning: Algorithms and Applications , 2016 .

[24]  Jia Hao,et al.  A quantitative approach to design alternative evaluation based on data-driven performance prediction , 2017, Adv. Eng. Informatics.

[25]  Ali Zeinal Hamadani,et al.  An Adjusted Decision Support System through Data Mining and Multiple Criteria Decision Making , 2013 .

[26]  Qingyuan Zhu,et al.  China's regional natural resource allocation and utilization: a DEA-based approach in a big data environment , 2017 .

[27]  B. Naderi,et al.  Clustering and ranking university majors using data mining and AHP algorithms: A case study in Iran , 2011, Expert Syst. Appl..

[28]  Simon Fong,et al.  Adaptive multi-objective swarm fusion for imbalanced data classification , 2018, Inf. Fusion.

[29]  C. Zeng,et al.  Management of urban land expansion in China through intensity assessment: A big data perspective , 2017 .

[30]  Patrick T. Hester,et al.  An Analysis of Multi-Criteria Decision Making Methods , 2013 .

[31]  Reza Farzipoor Saen,et al.  Assessing sustainability of supply chains by double frontier network DEA: A big data approach , 2017, Comput. Oper. Res..

[32]  Shu-Hsien Liao,et al.  Data mining techniques and applications - A decade review from 2000 to 2011 , 2012, Expert Syst. Appl..

[33]  Rommel N. Carvalho,et al.  Applying clustering and AHP methods for evaluating suspect healthcare claims , 2017, J. Comput. Sci..