Dear Colleagues,

Given the complexity of the socioeconomic environment, decision making is one of the most notable ventures, whose mission is to decide the best alternative under numerous qualitative and quantitative factors/criteria. Multiple-criteria decision-making (MCDM) methods and hybrid models are quickly emerging as useful methods for evaluating and improving alternatives. Through the gradual maturation of information technology and the growth of the data analysis environment, large amounts of data within organizations could be accumulated. Therefore, some data-driven MADM models which integrate machine learning/data mining and MCDM methods to help decision-makers select the best alternative in various industries have been developed. Data mining or machine learning techniques are primarily concerned with discovering hidden patterns and relationships in data to assist decision-makers making judgements. MCDM is mainly concerned with problems which require ranking, classification, and sorting based on multiple criteria or attributes. Combining data mining with MCDM methodologies to establish new or hybrid decision-making models involves combining the advantages of both methods in management sciences.

This Special Issue aims to collate original research papers that offer the latest developments and applications of MCDM, data mining, or hybrid models across various fields.

Prof. Dr. James Liou
Prof. Dr. Artūras Kaklauskas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• multiple-criteria decision-making (MCDM)
• data mining
• data driven
• machine learning
• knowledge-based systems
• hybrid multiple-criteria decision-making methods
• intelligent decision support systems
• optimization techniques
• soft computing
• application of mcdm methods
• site selection
• resource allocation
• supply chain management
• production management
• quality management
• risk management
• decision analysis for sustainable production and consumption
• group decision making
• MCDM theories
• MCDM in strategic management
• decision making
• hybrid decision-making analysis
• information technologies in decision making
• innovative applications of MCDM methods
• weighting approach
• technologies and techniques
• sustainability assessment data mining models and tools
• data mining result validation
• privacy concerns and ethics
• practical applications (government, international development, culture healthcare, education, media, insurance, Internet of Things, agriculture, industry)
• case studies
• impacts of data science
• quality of city life and data mining
• smart city and data mining
• behavioral change and data mining
• neuromarketing and data mining