A fuzzy approach to prioritise DEA ranked association rules

Shukla, Shekhar; Mohanty, B. K.; Kumar, Ashwani

doi:10.1504/ijbidm.2019.096842

Cited by 7 publications

(5 citation statements)

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“…Thus, these celebrity criteria need not be measured on the same scale, which makes them non-commensurable. This premise of real-world criteria for a potent research problem like celebrity selection reveals the dimension of uncertainty and fuzziness in the problem (Atlam et al, 2019;Shukla et al, 2019). We used fuzzy logic to quantify the qualitative and often linguistically expressed opinions (or levels of importance) of the DMs.…”

Section: Why the Mgfem And Fitradeoff Method?mentioning

confidence: 99%

“…We used fuzzy logic to quantify the qualitative and often linguistically expressed opinions (or levels of importance) of the DMs. The method used here is MGFEM (Biswas, 1995; Shukla et al , 2019). This method is a simple yet robust approach to handling opinions (in terms of fuzzy marks) of a group of DMs at different levels of satisfaction/agreement across importance classes.…”

Section: The Study and Methodologymentioning

confidence: 99%

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Celebrity selection in social media ecosystems: a flexible and interactive framework

Shukla

Dubey

2021

JRIM

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PurposeQuantitative objective studies on the problem of celebrity selection are lacking. Furthermore, existing research does not recognize the group decision-making nature and the possibility of customer involvement in celebrity or influencer selection for social media marketing. This study conceptualizes celebrity selection as a multi-attribute group decision-making problem while deriving the final ranking of celebrities/influencers using interactive and flexible criteria based on the value tradeoff approach. The article thus proposes and demonstrates a quantitative objective method of celebrity selection for a brand or campaign in an interactive manner incorporating customer's preferences as well.Design/methodology/approachEach decision-maker's preferences for celebrity selection criteria are objectively captured and converted into an overall group preference using a modified generalized fuzzy evaluation method (MGFEM). The final ranking of celebrities is then derived from an interactive and criteria-based value tradeoff approach using the flexible and interactive tradeoff method.FindingsThe approach gives a different ranking of celebrities for two campaigns based on group members' perceived importance of the selection criteria in different scenarios. This group includes decision-makers (DMs) from the brand, marketing communication agency and brand's customers. Further, each group member has an almost equal say in the decision-making based on fuzzy evaluation and an interactive and flexible value tradeoff approach to celebrity selection for receiving a rank order.Research limitations/implicationsThe approach uses secondary data on celebrities and hypothetical scenarios. Comparison with other methods is difficult, as no other study proposes a multi-criteria group decision-making approach to celebrity selection especially in a social media context.Practical implicationsThis approach can help DMs make more informed, objective and effective decisions on celebrity selection for their brands or campaigns. It recognizes that there are multiple stakeholders, including the end customers, each of whose views is objectively considered in the aspects of group decision-making through a fuzzy evaluation method. Further, this study provides a selection mechanism for a given context of endorsement by objectively and interactively encapsulating stakeholder preferences.Originality/valueThis robust and holistic approach to celebrity selection can help DMs objectively make consensual decisions with partial or complete information. This quantitative approach contributes to the literature on selection mechanisms of influencers, celebrities, social media opinion leaders etc. by providing a methodological aid that encompasses aspects of interactive group decision-making for a given context. Moreover, this method is useful to DMs and stakeholders in understanding and incorporating the effect of nature or context of the brand and the campaign type in the selection of a celebrity or an influencer.

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Section: Why the Mgfem And Fitradeoff Method?mentioning

confidence: 99%

Section: The Study and Methodologymentioning

confidence: 99%

Celebrity selection in social media ecosystems: a flexible and interactive framework

Shukla

Dubey

2021

JRIM

Self Cite

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“…Combined with the above calculation and Table 1, the enterprise lean management quality evaluation model can be expressed as follows [27,28]:…”

Section: Quality Evaluation Of Enterprise Lean Managementmentioning

confidence: 99%

Application of Data Mining in Effect Evaluation of Lean Management

Ding¹,

Li²,

2022

Scientific Programming

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Quantitative evaluation is an important part of enterprise diagnosis, which promotes the scientific and modern management of enterprises. At present, the existing enterprise management evaluation methods cannot complete the mining of enterprise index data, which leads to large error and low significance coefficient in enterprise management evaluation. Therefore, the application of data mining in enterprise lean management effect evaluation is put forward. The process and main functions of data mining are analyzed; data mining algorithm is used to establish the evaluation index system of lean management effect and calculate the index weight. Using the association rules method in data mining, according to the parameters of enterprise lean management level evaluation index and weight value, through the fuzzy set transformation idea, the fuzzy boundary of each index and factor is described by the membership degree, the fuzzy judgment matrix is constructed, and the final evaluation result is obtained by multilayer compound calculation. Experimental results show that this study has a high significance coefficient, and the proposed evaluation method of enterprise lean management effect has ideal accuracy and short time consumption. In practical application, the cumulative contribution rate is higher and has higher stability.

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“…[Choi et al, 2005] used ELECTREE II method to rank the association rules. [Shukla et al, 2019] proposed an approach based on DEA (Data Envelopment Analysis) to rank discovered association rules. DEA measures the efficiency of each discovered association rule based on multiple criteria.…”

Section: Data Mining Stepmentioning

confidence: 99%

Association Rule Mining to Shortlist Plant Phenolic Compounds Likely to Decrease Methane Emissions by Ruminants

Guillaume

Machebœuf

2022

Advances in Knowledge Discovery and Management

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The purpose of this work was to find phenolic compounds in plants that could act on ruminal fermentations to limit methane emissions by ruminants, in order to propose natural additives or food alternatives. We used a data mining method to extract class association rules that would identify compounds likely to have a significant effect. Such extraction usually generates a large number of rules. Our problem was to select the best rules, and thereby the most promising compounds. We carried out a new kind of extraction: mining for strongly expressed rules, that is to say rules that govern whether compounds are abundant in the plants. We propose two new interesting measures to evaluate the intensity of expression rules, and a new type rule visualization. Among the 1,075 phenolic compounds found in the 208 plants analysed, 7 promising compounds and 5 useful associations of compounds were shortlisted.

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A fuzzy approach to prioritise DEA ranked association rules

Cited by 7 publications

References 0 publications

Celebrity selection in social media ecosystems: a flexible and interactive framework

Celebrity selection in social media ecosystems: a flexible and interactive framework

Application of Data Mining in Effect Evaluation of Lean Management

Association Rule Mining to Shortlist Plant Phenolic Compounds Likely to Decrease Methane Emissions by Ruminants

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