Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM

Abstract

In many disciplines, the evaluation of algorithms for processing massive data is a challenging research issue. However, different algorithms can produce different or even conflicting evaluation performance, and this phenomenon has not been fully investigated. The motivation of this paper aims to propose a solution scheme for the evaluation of clustering algorithms to reconcile different or even conflicting evaluation performance. The goal of this research is to propose and develop a model, called decision-making support for evaluation of clustering algorithms (DMSECA), to evaluate clustering algorithms by merging expert wisdom in order to reconcile differences in their evaluation performance for information fusion during a complex decision-making process. The proposed model is tested and verified by an experimental study using six clustering algorithms, nine external measures, and four MCDM methods on 20 UCI data sets, including a total of 18,310 instances and 313 attributes. The proposed model can generate a list of algorithm priorities to produce an optimal ranking scheme, which can satisfy the decision preferences of all the participants. The results indicate our developed model is an effective tool for selecting the most appropriate clustering algorithms for given data sets. Furthermore, our proposed model can reconcile different or even conflicting evaluation performance to reach a group agreement in a complex decision-making environment.

Evaluation of Group Decision Making Based on Group Preferences Under a Multi-Criteria Environment

“…In Stage 2, the CRM model is presented to reconcile different or even conflicting rankings generated by the eight GDM methods used in Stage 1. In this process, the 80−20 rule (Wu et al, 2020) is applied to focus on the most important positions of the rankings associated with the number of observations, and it can largely consider the most important group preferences of all the participants.…”

“…Much research has focused on developing new algorithms or designing new models. However, there are situations in which different MCDM methods can produce different or even conflicting rankings (Peng et al, 2011;Kou et al, 2012;Wu et al, 2018Wu et al, , 2020, and how to reconcile these different or even conflicting rankings in GDM becomes an important research issue which has not yet been fully studied and investigated.…”

“…This Stage is to identify and achieve an optimal ranking scheme by a consensus recognition model (CRM), based on group preferences of evaluation results of GDM methods generated in Stage 1. The CRM model includes three steps as follows (Wu et al, 2020):…”

“…Data are a strategic asset, decision makers are struggling to gain useful, valuable information, rules, patterns or business insights out of data for decision support. However, decision making process is an extremely complex process because of complexity of the systems considered and competitive conflicts of interest among multi-stakeholders (Ascough II et al, 2008;Wu et al, 2018Wu et al, , 2020. Individual decision greatly affects or even reduces the efficiency of decision-making.…”

Hybrid TODIM Method with Crisp Number and Probability Linguistic Term Set for Urban Epidemic Situation Evaluation

“…In emergency medical management, decision-making process has become more and more complex because many of the attributes are difficult to quantify [11][12][13] and decisions are usually made with subjective preference information in human perceptual timeframes under pressure, lack of knowledge, and data cases [14,15]. Furthermore, their effectiveness can be constrained due to the complexity of the decision-making process and the intricacy of systems [13,16]. How to effectively respond to the accidental medical emergencies and evaluate the severity of urban COVID-19 epidemic situation during a complex environment has become a global challenge.…”