Assessing partially ordered clustering in a multicriteria comparative context

Rosenfeld, John L.; Smet, Yves De; Debeir, Olivier; Decaestecker, Christine

doi:10.1016/j.patcog.2021.107850

Cited by 12 publications

(2 citation statements)

References 29 publications

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“…Measures developed to assess (external) cluster validity are very similar to the measures we develop. For instance, the intra-and inter-heterogeneity measures developed in Rosenfeld et al, (2021) are fully in line with our within-group and betweengroup similarity measures. Given this methodological accordance, we believe our suggested bootstrapping techniques in support of statistical inference have the potential to contribute to this literature.…”

Section: Discussionsupporting

confidence: 70%

External validity of multi-criteria preference data obtained from non-random sampling: measuring cohesiveness within and between groups

2022

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An important component of multi-criteria decision analysis (MCDA) in the public sector is the elicitation and aggregation of preference data collected via surveys into the relative importance of the criteria for the decision at hand. These aggregated preference data, usually in the form of mean weights on the criteria, are intended to represent the preferences of the relevant population overall. However, random sampling is often not feasible for public-sector MCDA for logistical reasons, including the expense involved in identifying and recruiting participants. Instead, non-random sampling methods such as convenience, purposive or snowball sampling are widely used. Nonetheless, provided the preference data collected are sufficiently ‘cohesive’ in terms of the extent to which the weights of the individuals belonging to the various exogenously defined groups in the sample are similar, non-random sampling can still produce externally valid aggregate preference data. We explain a method for measuring cohesiveness using the Kemeny and Hellinger distance measures, which involve measuring the ‘distance’ of participants’ weights (and the corresponding rankings of the criteria) from each other, within and between the groups respectively. As an illustration, these distance measures are applied to data from a MCDA to rank non-communicable diseases according to their overall burden to society. We conclude that the method is useful for evaluating the external validity of preference data obtained from non-random sampling.

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Section: Discussionsupporting

confidence: 70%

External validity of multi-criteria preference data obtained from non-random sampling: measuring cohesiveness within and between groups

2022

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“…In this scenario, researchers have created innovative and outstanding clustering algorithms in the MCDA, including ELEC-TRE-SORT [15], Flowsort [16], TODIM-Sort [17], PROAFTN [18], and PAIRCLASS [19]. On the other hand, numerous types of research studies have been conducted to address the problem of undefned classes from the MCDA perspective [20] and their performance evaluation [21]. Tis area highlights three types of problems in clustering: relational, nonrelational, and ordered clustering (Boujelben [22]; Meyer and Olteanu [23]).…”

Section: Introductionmentioning

confidence: 99%

Generalized Ordered Intuitionistic Fuzzy C-Means Clustering Algorithm Based on PROMETHEE and Intuitionistic Fuzzy C-Means

Bashir,

Rashid,

Bashir

2023

International Journal of Intelligent Systems

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The problem of ordered clustering in the context of decision-making with multiple criteria has garnered significant interest from researchers in the field of management science and operational research. In real-world scenarios, the datasets often exhibit imprecision or uncertainty, which can lead to suboptimal ordered-clustering outcomes. However, the intuitionistic fuzzy c-means (IFCM) clustering algorithm enhances the accuracy and effectiveness of decision-making processes by effectively handling uncertain dataset information for clustering. Therefore, we propose a new clustering algorithm, called the generalized ordered intuitionistic fuzzy c-means (G-OIFCM), based on PROMETHEE and the IFCM clustering algorithm. Different from the classical IFCM clustering algorithm, we use positive flow ( φ + s i ∈ 0 , 1 ) and negative flow ( φ − s i ∈ 0 , 1 ) of PROMETHEE to generate ordered clusters within the intuitionistic environment. We define a new objective function based on the positive and negative flow of the PROMETHEE and IFCM clustering algorithm, whose properties are mathematically justified in terms of convergence and optimization. The performance of the proposed algorithm is evaluated using two different real-world datasets to assess both the ordered clustering and the quality of partitioning. To demonstrate the effectiveness of G-OIFCM, a comparison is conducted with three other algorithms: fuzzy c-means (FCM), ordered fuzzy c-means (OFCM), and an adaptive generalized intuitionistic fuzzy c-means (G-IFCM). The results demonstrate the effectiveness of G-OIFCM in enhancing optimal ordered clustering and utility when dealing with uncertainty in datasets.

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