Multi-Attribute Decision Making
Mojtaba Movahedi; Mahdi Homayounfar; Mehdi Fadaei; Mansour Soufi
Abstract
Purpose: Clustering algorithms are useful tools for understanding data structure and classifying them into different data sets. Due to the importance of using these algorithms in analyzing financial market data that have a high volume and scope, this study in order to select the best clustering algorithm ...
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Purpose: Clustering algorithms are useful tools for understanding data structure and classifying them into different data sets. Due to the importance of using these algorithms in analyzing financial market data that have a high volume and scope, this study in order to select the best clustering algorithm for clustering companies listed on the Tehran Stock Exchange in the field of finance from It has used different clustering algorithms and evaluated the validity of these algorithms and selected the best algorithm.Methodology: This research is applied in terms of purpose and descriptive in terms of implementation method and is of quantitative type (mathematical modeling). The statistical population of the research includes 403 companies listed on the Tehran Stock Exchange in 2019, whose performance has been evaluated based on four financial criteria.Findings: After clustering the surveyed companies by five clustering algorithms, namely K-means, EM, COBWEB, density-based algorithm and ward method, seven indicators RS, DB, Dun, SD, Purity, Entropy and Time were used to evaluate the algorithms. Finally, the total performance of the algorithms was analyzed based on TOPSIS, VICOR and DEA methods. Based on the results, K-means has a better performance in clustering based on the financial data sets.Originality/Value: Since no clustering algorithm can have the best performance in all measurements for each data set, this study uses a combination of multiple criteria to analyze data clustering algorithms related to the field of financial performance appraisal. Companies have provided suggestions and the results of this study have been used effectively for investors in the field of finance, which leads to the optimal choice of investment portfolio.
Multi-Attribute Decision Making
Saba Amiri; Saeed Setayeshi
Abstract
Purpose: Neuromarketing is an interdisciplinary and emerging field which can be used in order to relate consumer behavior to neuroscience. So, in recent decades, the importance and interest in buying sustainable products for protecting the environment has been increased. Thus, the present study was done ...
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Purpose: Neuromarketing is an interdisciplinary and emerging field which can be used in order to relate consumer behavior to neuroscience. So, in recent decades, the importance and interest in buying sustainable products for protecting the environment has been increased. Thus, the present study was done with the aim of fuzzy analytic hierarchy process of neuromarketing evaluation criteria for sustainable products.Methodology: The research was performed with a quantitative approach and by using multiple-criteria decision analysis. For this purpose, in order to gain a deep understanding of the subject and collecting useful data, after carefully reviewing the related studies, the views of 16 experts were collected using a fuzzy hierarchical researcher-made questionnaire, which the inconsistency rate of the questionnaires confirmed reliability of them. Also, sensitivity analysis was used to ensure.Findings: The results showed that the criteria for evaluating neuromarketing are in seven categories, which based on FAHP are: accuracy, biasness, exploration of memory and emotion, information quality, usefulness, time saving, cost, respectively. Also, the alternatives of marketing for sustainable products affected by neuromarketing in order of priority are: advertising, product design and development, branding, consumer decision, pricing and distribution. Sensitivity analysis also showed that the research findings are confirmed, but in the case of two criteria of biasness and exploration of memory and emotions, there is a possibility of displacement.Originality/Value: Neuromarketing, due to the provision of high-precision and high-quality information and the reduction of bias in the analysis of results, provides the possibility of predicting consumer buying behavior and affects the marketing mix of sustainable products.
Fuzzy Optimization
Madineh Farnam; Majid Darehmiraki
Abstract
Purpose: In working with Interval-valued intuitionistic fuzzy sets according to considering the membership and non-membership function simultaneously, as well as the interval of the data type, we face to a lot of flexibility to allocate data by the decision maker. Comparison ...
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Purpose: In working with Interval-valued intuitionistic fuzzy sets according to considering the membership and non-membership function simultaneously, as well as the interval of the data type, we face to a lot of flexibility to allocate data by the decision maker. Comparison between them, as one of the first concepts in the decision-making process, does not seem so simple. For this purpose, in this paper we present an integrated and efficient method and a new way to prioritize interval-valued intuitionistic fuzzy numbers. Then we apply this method to assess the qualitative qualification of contractors.Methodology: Use interval valued intuitionistic fuzzy sets along with multi criteria decision making.Findings: New ranking method of interval valued intuitionistic fuzzy sets is apllied in evaluating operational units. In addition, by giving a practical example while describing the process performance, the output of the work is observed.Originality/Value: A new method is proposed to determine the preference between interval valued intuitionistic fuzzy sets. In addition, an efficiency process is introduced to assess the qualitative qualification of contractors.