A Multicriteria Decision Making Approach for Estimating the Number of Clusters in a Data Set

Determining the number of clusters in a data set is an essential yet difficult step in cluster analysis. Since this task involves more than one criterion, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes a multiple criteria decision making (MCDM)-based approach to estimate the number of clusters for a given data set. In this approach, MCDM methods consider different numbers of clusters as alternatives and the outputs of any clustering algorithm on validity measures as criteria. The proposed method is examined by an experimental study using three MCDM methods, the well-known clustering algorithm–k-means, ten relative measures, and fifteen public-domain UCI machine learning data sets. The results show that MCDM methods work fairly well in estimating the number of clusters in the data and outperform the ten relative measures considered in the study

Choice Rules with Size Constraints for Multiple Criteria Decision Making

In outranking methods for Multiple Criteria Decision Making (MCDM), pair-wise comparisons of alternatives are often summarized through a fuzzy preference relation. In this paper, the binary preference relation is extended to pairs of subsets of alternatives in order to define on this basis a scoring function over subsets. A choice rule based on maximizing score under size constraint is studied, which turns to formulate as solving a sequence of classical location problems. For comparison with the kernel approach, the interior stability property of the selected subset is discussed and analyzed.Combinatorial optimization; Fuzzy preferences; Integer Programming; Location; Multiple Criteria Decision Aid

Application of multiple criteria decision methods in space exploration initiative design and planning

Fellowship activities were directed towards the identification of opportunities for application of the Multiple Criteria Decision Making (MCDM) techniques in the Space Exploration Initiative (SEI) domain. I identified several application possibilities and proposed demonstration application in these three areas: evaluation and ranking of SEI architectures, space mission planning and selection, and space system design. Here, only the first problem is discussed. The most meaningful result of the analysis is the wide separation between the two top ranked architectures, indicating a significant preference difference between them. It must also be noted that the final ranking reflects, to some extent, the biases of the evaluators and their understanding of the architecture

Multiple Criteria Decision Making: Method Selection And Application To Three Contrasting Agricultural Case Studies

Agribusiness, farm business and agricultural-environmental decisions which varied in their characteristics were used to evaluate multiple criteria decision making (MCDM) in an agricultural context. This paper discusses differences between the case studies, strengths and weaknesses of the methods used, and the success of the MCDM process based on participants’ expectations and experiences. While MCDM can help identify the best decision, the main benefits identified in using MCDM included better understanding of their own and other’s perspectives, a means to explain the decision and a structured way to work through the decision process. Key problem areas identified included time limitations, understanding and ownership.Multiple criteria decision making, agriculture, decision making, Agribusiness, Agricultural Finance, Consumer/Household Economics, Crop Production/Industries, Environmental Economics and Policy, Farm Management, Land Economics/Use,

Kriterijų svorių perskaičiavimas Bajeso metodu

Multiple Criteria Decision Making (MCDM) methods are effectively used in decision making tasks. The weights of criteria are an integral part of MCDM methods. The paper proposes the Bayesian approach to recalculate the weights of the criteria, when the decision-maker takes into account the opinions of other expert groups. Recalculation is relevant when the selection is individualized by the opinion of separate expert group. In this paper the distance learning course was chosen by separate group of experts, using SAW and TOPSIS methods and recounted criteria weights by Bayesian method.Sprendimo priėmimo uždaviniuose efektyviai naudojami daugiakriteriai sprendimo priėmimo (angl. Multiple criteria decision making, MCDM) metodai. MCDM metodų sudedamoji dalis – kriterijų svoriai. Straipsnyje pasiūlytas Bajeso metodo taikymas kriterijų svoriams perskaičiuoti, kai sprendimą priimantis asmuo (SPA) atsižvelgia į kitų ekspertinių grupių nuomones. Perskaičiavimas aktualus, kai pasirinkimas individualizuojamas pagal atskiros ekspertų grupės nuomonę. Straipsnyje Bajeso metodo taikymas kriterijų svorių perskaičiavimui iliustruojamas vertinant nuotolinių studijų kursų kokybę. Kurso kokybė yra nustatoma SAW ir TOPSIS metodais

Comments on “Multiple criteria decision making (MCDM) methods in economics: an overview”

This paper offers comments on a previously published paper, titled “Multiple criteria decision making (MCDM) methods in economics: an overview,” by Zavadskas and Turskis (2011). The paper's authors made great efforts to summarize MCDM methods but may have failed to consider several important new concepts and trends in the MCDM field for solving actual problems. First, the traditional model assumes the criteria are independently and hierarchically structured; however, in reality, problems are often characterized by interdependent criteria and dimensions and may even exhibit feedback-like effects. Second, relatively good solutions from the existing alternatives are replaced by aspiration levels to fit today's competitive markets. Third, the emphasis in the field has shifted from ranking and selection when determining the most preferable approaches to performance improvement of existing methods. Fourth, information fusion techniques, including the fuzzy integral method, have been developed to aggregate the performances. Finally, the original fixed resources in multi-objective programming are divided such that both decision and objective spaces are changeable. In this paper, we add new concepts and provide comments that could be thought of as an attempt to complete the original paper

A REVIEW OF APPLICATIONS OF MULTIPLE - CRITERIA DECISION-MAKING TECHNIQUES TO FISHERIES

Management of public resources, such as fisheries, is a complex task. Society, in general, has a number of goals that it hopes to achieve from the use of public resources. These include conservation, economic, and social objectives. However, these objectives often conflict, due to the varying opinions of the many stakeholders. It would appear that the techniques available in the field of multiple-criteria decision-making (MCDM) are well suited to the analysis and determination of fisheries management regimes. However, to date, relatively few publications exist using such MCDM methods compared to other applicational fields, such as forestry, agriculture, and finance. This paper reviews MCDM applied to fishery management by providing an overview of the research published to date. Conclusions are drawn regarding the success and applicability of these techniques to analyzing fisheries management problems.Resource /Energy Economics and Policy,

Development of MCDM Methods – in Honour of Professor Edmundas Kazimieras Zavadskas on the Occasion of His 70th Birthday

Multiple Criteria Decision Making (MCDM) substantially evolved duringthe past decades and became one of the most important areas in OperationalResearch/Management Science. The article presents a review of extensive scientificwork of Professor Edmundas Kazimieras Zavadskas on development of MCDM methodson the occasion of his 70th birthday. The article also highlights his researchcarrier, and lists some of his publications