Similarity–trust network for clustering‐based consensus group decision‐making model

Ahlim, Wan Syahimi Afiq Wan; Kamis, Nor Hanimah; Ahmad, Sharifah Aniza Sayed; Chiclana, Francisco

doi:10.1002/int.22610

Cited by 12 publications

(6 citation statements)

References 31 publications

(37 reference statements)

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“…All the methods we use extensively like VIKOR, TOPSIS, ELECTRE, PROMETHEE are used to analyze the best alternatives based on certain factors and select the optimal alternative. 45,46 These MCDM methods are used by most researchers and industry experts in various fields such as supply chain management, construction and project management, information technology management, energy, environmental and sustainability, GIS landslide susceptibility mapping, and so forth. 47,48 When using MCDM technology for material selection or machine tool selection, we can easily rank with the actual performance of the alternatives.…”

Section: Discussionmentioning

confidence: 99%

Decision‐making technique to achieve stratified target performance: Analyze science and technology innovation policy investment of South Korea

Geetha

Jeon

2021

Int J of Intelligent Sys

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From the entire multi-criteria decision-making methodology literature review, we begin to understand all the decision-making techniques, which help decision-makers choose the best alternative within specific criteria based on past or current performance levels. But when we use these technologies in investment plans, renewable energy policy plans, strategic plans, and so forth, we should consider future performance values in the decision-making process. As far as the author knows, no decision-making method can rank alternatives with future performance target values. This study article introduced a novel decision-making technique, namely DEcision Making Technique to Achieve Stratified TArget Performance (DEMTASTAP), for ranking the alternatives with the future performance values. In recent years, the concept of stratification has become the central concept in the decision-making process. Here, the target level of each alternative under each factor regard as the stratification level. We validate the introduced DEMTASTAP method by testing the completeness of the ranking, the correlation of the order, the inversion of the scale, and the sensitivity.Demonstrate the efficiency of the DEMTASTAP method by solving the Science and Technology Innovation Policy (STIP) investment issues. Using the introduced

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Section: Discussionmentioning

confidence: 99%

Decision‐making technique to achieve stratified target performance: Analyze science and technology innovation policy investment of South Korea

Geetha

Jeon

2021

Int J of Intelligent Sys

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“…An important question lingers about what exactly counts as an “expert consensus.” Alas, there is no precise ratio or number that Kuhn (1962), Nersessian (1992), or any other philosopher of science has identified, and neither will entrepreneurship scholars be able to easily quantify “consensus.” Most consensus scholarship rejects definitions that rely on full unanimity of opinion in favor of a “soft” consensus that refers to a level of agreement (and dissension) among experts (Kacprzyk & Fedrizzi, 1986). While a “consensus” may be (and has been) empirically identifiable (e.g., Ahlim et al, 2022; Wang et al, 2021), we simply note that, as with many fuzzy concepts, scholars (and entrepreneurs and their stakeholders) will choose cutoffs and measures suited to their research questions for methodological purposes. We also note that assessments of consensus can refer either to a degree of agreement (determining if consensus exists), as we do above, or it can refer to the distance of an opinion from the consensus (e.g., Chiclana et al, 2015), which correspond to our notion of “how rogue” an opinion or claim is.…”

Section: Knowledge Claimsmentioning

confidence: 99%

Rogue Entrepreneurship

McBride

Packard

Clark

2023

Entrepreneurship Theory and Practice

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We suggest a new category of “rogue entrepreneurship,” that describes entrepreneurial activity where the core business idea violates established or expert consensus, to be contrasted with “conforming entrepreneurship,” where it does not. There are large entrepreneurial rents hidden behind a bulwark of expert consensus that predicts doom for a venture based upon a rogue and unlikely claim. The “rogue” cases, where the predominant assessment context is different from the entrepreneur’s, result in broad skepticism against the entrepreneurial claim. We explain what rogue entrepreneurship is and how it works.

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“…Although Equation ( 11) is derived from Equation (10), and Equation ( 10) contains no trade-off parameter for trading-off the lower-order part and the higher-order part, a trade-off analysis is conducted to investigate how the trade-off between the lower-order part and the higher-order part in Equation ( 11) affects the performance of the GHOC method. To this end, in this experiment, a trade-off parameter λ is inserted into Equation (11), which results in…”

Section: Trade-off Analysismentioning

confidence: 99%

“…In the past few years, motif-based higher-order community detection has attracted a large amount of attention in the field of network analysis. [1][2][3][4][5] Compared with the conventional lowerorder community detection only utilizing the lower-order connectivity pattern which can be captured at the level of individual nodes and edges, [6][7][8][9][10] higher-order community detection mainly relies on the higher-order connectivity pattern at the level of small subnetworks, namely motif. 1 In particular, motif is a dense subnetwork occurring in complex networks at numbers that are significantly higher than those in randomized networks preserving the same degree of nodes.…”

Section: Introductionmentioning

confidence: 99%

GHOC: A generative model for hybrid‐order community detection

Huang

Tang

et al. 2022

Int J of Intelligent Sys

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Recently, hybrid-order community detection has been proposed for addressing the hypergraph fragmentation issue suffered by the motif-based higher-order community detection. However, the existing attempts of hybrid-order community detection inadvertently damage the lower-order connectivity pattern and the higher-order connectivity pattern when constructing the fusion model. Additionally, like the higher-order community detection approaches, they also adopt a two-phase strategy that separately applies the existing graph node clustering methods to the proximity matrix derived from the lower-order connectivity pattern and the higher-order connectivity pattern. Therefore, the higher-order connectivity pattern is only utilized for constructing proximity matrix and hence has no direct effect on the final community results. In this paper, to address the above issues, we propose a Generative model for Hybrid-Order Community detection (GHOC). The main idea lies in defining a likelihood function of a generative model that finds the optimal community membership strength vectors of nodes, based on which the original lower-order connectivity pattern and the higher-order connectivity pattern can be directly reconstructed simultaneously. From the community membership strength vectors, the final community structure can be derived. Extensive

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Similarity–trust network for clustering‐based consensus group decision‐making model

Cited by 12 publications

References 31 publications

Decision‐making technique to achieve stratified target performance: Analyze science and technology innovation policy investment of South Korea

Decision‐making technique to achieve stratified target performance: Analyze science and technology innovation policy investment of South Korea

Rogue Entrepreneurship

GHOC: A generative model for hybrid‐order community detection

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