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Article
Publication date: 8 August 2016

Mahsan Esmaeilzadeh, Bijan Abdollahi, Asadallah Ganjali and Akbar Hasanpoor

The purpose of this paper is to introduce an evaluation methodology for employee profiles that will provide feedback to the training decision makers. Employee profiles play a…

Abstract

Purpose

The purpose of this paper is to introduce an evaluation methodology for employee profiles that will provide feedback to the training decision makers. Employee profiles play a crucial role in the evaluation process to improve the training process performance. This paper focuses on the clustering of the employees based on their profiles into specific categories that represent the employees’ characteristics. The employees are classified into following categories: necessary training, required training, and no training. The work may answer the question of how to spend the budget of training for the employees. This investigation presents the use of fuzzy optimization and clustering hybrid model (data mining approaches) as a fuzzy imperialistic competitive algorithm (FICA) and k-means to find the employees’ categories and predict their training requirements.

Design/methodology/approach

Prior research that served as an impetus for this paper is discussed. The approach is to apply evolutionary algorithms and clustering hybrid model to improve the training decision system directions.

Findings

This paper focuses on how to find a good model for the evaluation of employee profiles. The paper introduces the use of artificial intelligence methods (fuzzy optimization (FICA) and clustering techniques (K-means)) in management. The suggestion and the recommendations were constructed based on the clustering results that represent the employee profiles and reflect their requirements during the training courses. Finally, the paper proved the ability of fuzzy optimization technique and clustering hybrid model in predicting the employee’s training requirements.

Originality/value

This paper evaluates employee profiles based on new directions and expands the implication of clustering view in solving organizational challenges (in TCT for the first time).

Details

International Journal of Intelligent Computing and Cybernetics, vol. 9 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 10 June 2014

Diana Janeth Lancheros-Cuesta, Angela Carrillo-Ramos and Jaime A. Pavlich-Mariscal

This article aims to propose an adaptation algorithm that combines the analytical hierarchy process (AHP), a rule-based system, and a k-means clustering algorithm. Informatic…

1130

Abstract

Purpose

This article aims to propose an adaptation algorithm that combines the analytical hierarchy process (AHP), a rule-based system, and a k-means clustering algorithm. Informatic tools are very useful to enhance the learning process in the classroom. The large variety of these tools require advanced decision-making techniques to select parameters, such as student profiles and preferences, to adjust content and information display, according to specific characteristics and necessities of students. They are part of the Kamachiy–Idukay (KI), a platform to offer adaptative educational services to students with learning difficulties or disabilities.

Design and Methodology

The design and implementation of the adaptation algorithm comprises the following phases: utilization of the AHP to determine the most important student parameters, parameter to take into account in the adaptation process, such as preferences, learning styles, performance in language, attention and memory aspects and disabilities; designing the first part of the adaptation algorithm, based on a rule-based system; designing the second part of the adaptation algorithm, based on k-means clustering; integration of the adaptation algorithm to KI; and validation of the approach in a primary school in Bogotá (Colombia).

Approach

The main approach is the application of computational techniques, namely, rule-based systems and k-means clustering, plus an AHP prioritization at design time to yield a system to support the teaching–learning process for students with disabilities or learning difficulties.

Findings

The algorithm found several groups of students with specific learning difficulties that required adapted activities. The algorithm also prioritized activities according to learning style and preferences. The results of the application of this system in a real classroom yielded positive results.

Limitations of the research

The algorithm performs adaptation for students with mild disabilities or learning difficulties (language, attention and memory). The algorithm does not address severe disabilities that could greatly affect cognitive abilities.

Contributions

The main contribution of this paper is an adaptation algorithm with the following distinctive characteristics, namely, designed utilizing the AHP, which ensures a proper prioritization of the student characteristics in the adaptation process, and utilizes a rule-based system to identify different adaptation scenarios and k-means clustering to group students with similar adaptation requirements.

Details

International Journal of Web Information Systems, vol. 10 no. 2
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 23 August 2011

Ch. Aswani Kumar

The purpose of this paper is to introduce a new hybrid method for reducing dimensionality of high dimensional data.

Abstract

Purpose

The purpose of this paper is to introduce a new hybrid method for reducing dimensionality of high dimensional data.

Design/methodology/approach

Literature on dimensionality reduction (DR) witnesses the research efforts that combine random projections (RP) and singular value decomposition (SVD) so as to derive the benefit of both of these methods. However, SVD is well known for its computational complexity. Clustering under the notion of concept decomposition is proved to be less computationally complex than SVD and useful for DR. The method proposed in this paper combines RP and fuzzy k‐means clustering (FKM) for reducing dimensionality of the data.

Findings

The proposed RP‐FKM is computationally less complex than SVD, RP‐SVD. On the image data, the proposed RP‐FKM has produced less amount of distortion when compared with RP. The proposed RP‐FKM provides better text retrieval results when compared with conventional RP and performs similar to RP‐SVD. For the text retrieval task, superiority of SVD over other DR methods noted here is in good agreement with the analysis reported by Moravec.

Originality/value

The hybrid method proposed in this paper, combining RP and FKM, is new. Experimental results indicate that the proposed method is useful for reducing dimensionality of high‐dimensional data such as images, text, etc.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 4 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 6 October 2023

Jie Yang, Manman Zhang, Linjian Shangguan and Jinfa Shi

The possibility function-based grey clustering model has evolved into a complete approach for dealing with uncertainty evaluation problems. Existing models still have problems…

Abstract

Purpose

The possibility function-based grey clustering model has evolved into a complete approach for dealing with uncertainty evaluation problems. Existing models still have problems with the choice dilemma of the maximum criteria and instances when the possibility function may not accurately capture the data's randomness. This study aims to propose a multi-stage skewed grey cloud clustering model that blends grey and randomness to overcome these problems.

Design/methodology/approach

First, the skewed grey cloud possibility (SGCP) function is defined, and its digital characteristics demonstrate that a normal cloud is a particular instance of a skewed cloud. Second, the border of the decision paradox of the maximum criterion is established. Third, using the skewed grey cloud kernel weight (SGCKW) transformation as a tool, the multi-stage skewed grey cloud clustering coefficient (SGCCC) vector is calculated and research items are clustered according to this multi-stage SGCCC vector with overall features. Finally, the multi-stage skewed grey cloud clustering model's solution steps are then provided.

Findings

The results of applying the model to the assessment of college students' capacity for innovation and entrepreneurship revealed that, in comparison to the traditional grey clustering model and the two-stage grey cloud clustering evaluation model, the proposed model's clustering results have higher identification and stability, which partially resolves the decision paradox of the maximum criterion.

Originality/value

Compared with current models, the proposed model in this study can dynamically depict the clustering process through multi-stage clustering, ensuring the stability and integrity of the clustering results and advancing grey system theory.

Details

Grey Systems: Theory and Application, vol. 14 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 1 February 2016

C.H. Wu, G.T.S. Ho, C.H.Y. Lam, W.H. Ip, K.L. Choy and Y.K. Tse

The purpose of this paper is to present a novel approach for niche-market tour identification, with the objective to obtain a better segmentation of target tourists and support…

2308

Abstract

Purpose

The purpose of this paper is to present a novel approach for niche-market tour identification, with the objective to obtain a better segmentation of target tourists and support the design of tourism products. A proposed system, namely the Niche Tourism Identification System (NTIS) was implemented based on the proposed scheme and its functionality was showcased in a case study undertaken with a local travel agency.

Design/methodology/approach

The proposed system implements automated customer market segmentation, based on similar characteristics that can be collected from potential customers. After that, special-interest tourism-based market strategies and products can be designed for the potential customers. The market segmentation is conducted using a GA-based k-means clustering engine (GACE), while the parameter setting is controlled by the travel agents.

Findings

The proposed NTIS was deployed in a real-world case study which helps a local travel agency to determine the various types of niche tourism found in the existing market in Hong Kong. Its output was reviewed by experience tour planners. It was found that with the niche characteristics can be successfully revealed by summarizing the possible factors within the potential clusters in the existing database. The system performed consistently compared to human planners.

Originality/value

To the best of the authors’ knowledge, although some alternative methods for segmenting travel markets have been proposed, few have provided any effective approaches for identifying existing niche markets to support online inquiry. Also, GACE has been proposed to compensate for the limitations that challenge k-means clustering in binding to a local optimum and for its weakness in dealing with multi-dimensional space.

Open Access
Article
Publication date: 8 March 2021

Mamdouh Abdel Alim Saad Mowafy and Walaa Mohamed Elaraby Mohamed Shallan

Heart diseases have become one of the most causes of death among Egyptians. With 500 deaths per 100,000 occurring annually in Egypt, it has been noticed that medical data faces a…

1081

Abstract

Purpose

Heart diseases have become one of the most causes of death among Egyptians. With 500 deaths per 100,000 occurring annually in Egypt, it has been noticed that medical data faces a high-dimensional problem that leads to a decrease in the classification accuracy of heart data. So the purpose of this study is to improve the classification accuracy of heart disease data for helping doctors efficiently diagnose heart disease by using a hybrid classification technique.

Design/methodology/approach

This paper used a new approach based on the integration between dimensionality reduction techniques as multiple correspondence analysis (MCA) and principal component analysis (PCA) with fuzzy c means (FCM) then with both of multilayer perceptron (MLP) and radial basis function networks (RBFN) which separate patients into different categories based on their diagnosis results in this paper, a comparative study of the performance performed including six structures such as MLP, RBFN, MLP via FCM–MCA, MLP via FCM–PCA, RBFN via FCM–MCA and RBFN via FCM–PCA to reach to the best classifier.

Findings

The results show that the MLP via FCM–MCA classifier structure has the highest ratio of classification accuracy and has the best performance superior to other methods; and that Smoking was the most factor causing heart disease.

Originality/value

This paper shows the importance of integrating statistical methods in increasing the classification accuracy of heart disease data.

Details

Review of Economics and Political Science, vol. 6 no. 3
Type: Research Article
ISSN: 2356-9980

Keywords

Article
Publication date: 4 March 2024

Tarek Chebbi, Hazem Migdady, Waleed Hmedat and Maha Shehadeh

The price clustering behavior is becoming a core part of the market efficiency theory especially with the development of trading strategies and the occurrence of major and…

Abstract

Purpose

The price clustering behavior is becoming a core part of the market efficiency theory especially with the development of trading strategies and the occurrence of major and unprecedented shocks which have led to severe inquiry regarding asset price dynamics and their distribution. However, research on emerging stock market is scant. The study contributes to the literature on price clustering by investigating an active emerging stock market, the Muscat stock market one of the Arabian Gulf Markets.

Design/methodology/approach

This research adopts the artificial intelligence technique and other statistical estimation procedure in understanding the price clustering patterns in Muscat stock market and their main determinants.

Findings

The findings reveal that stock prices are marked by clustering behavior as commonly highlighted in the previous studies. However, we found strong evidence of price preferences to cluster on numbers closer to zero than to one. We also show that the nature of firm’s activity matters for price clustering behavior. In addition, firms with traded bonds in Oman market experienced a substantial less stock price clustering than other firms. Clustered stock prices are more likely to have higher prices and higher volatility of price. Finally, clustering raised when the market became highly uncertain during the Covid-19 crisis especially for the financial firms.

Originality/value

This study provides novel results on price clustering literature especially for an active emerging market and during the Covid-19 pandemic crisis.

Details

Review of Behavioral Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1940-5979

Keywords

Open Access
Article
Publication date: 15 December 2023

Nicola Castellano, Roberto Del Gobbo and Lorenzo Leto

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on…

Abstract

Purpose

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

Design/methodology/approach

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

Findings

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

Practical implications

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

Originality/value

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Details

International Journal of Productivity and Performance Management, vol. 73 no. 11
Type: Research Article
ISSN: 1741-0401

Keywords

Abstract

Purpose

The aim of this research was to evaluate the maturity level of strategic communication management implemented by Brazilian startups.

Design/methodology/approach

This study employed the analytic hierarchy process (AHP), survey and Grey Fixed Weight Clustering modeling techniques. Three experts with extensive academic and practical experience in the subject participated in the AHP process, providing their opinions on the relative importance of eight variables associated with the topic under investigation, thus enabling their prioritization. Concurrently, data were collected through a survey from 23 respondents who have extensive knowledge about the realities of Brazilian startups. The weights derived from the AHP and the survey data were utilized in the Grey Fixed Weight Clustering modeling.

Findings

Based on the opinions of the 23 respondents, the level of implementation of practices related to strategic management, brand management, external image management and internal communication management is superficial. In addition, according to the majority of experts, Brazilian startups exhibited a medium level of maturity to address the key challenges related to communication management. Furthermore, this study reveals that the variables “financial resources allocation,” “stakeholder relationship” and “brand management” were deemed the most significant for the model.

Originality/value

The contributions presented herein can be beneficial for both researchers and startup managers seeking to enhance communication strategies in their organizations. This research also contributes by highlighting how grey systems theory can be extremely useful for conducting decision-making analyses in the context of startups, which is characterized by uncertainty and imprecise information.

Details

Grey Systems: Theory and Application, vol. 14 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 4 December 2023

Chebli Youness, Pierre Valette-Florence and Cynthia Assaf

The purpose of this research is to extend the results of previous studies regarding corporate reputation scales and identify new and specific items relevant for studying global…

Abstract

Purpose

The purpose of this research is to extend the results of previous studies regarding corporate reputation scales and identify new and specific items relevant for studying global corporate reputation from a customer’s point of view.

Design/methodology/approach

This research was based on the qualitative projective “Album on Line” (AOL) technique. The authors used a sample of 12 French consumers distributed equally between affective and cognitive scenarios. An individual-difference multidimensional scaling approach (INDSCAL) was applied to display the overall semantic space among generated items.

Findings

The exploratory AOL approach generated 62 items related to both cognitive and affective orientations characterizing online and offline corporate reputation. The results uncovered six semantic clusters for each scenario. All in all, seven new items could be added in the process of building a new global corporate reputation measurement scale by adding: avant-garde, singularity, exclusivity, savings, return policy, freeness and speed.

Research limitations/implications

This research makes it possible to propose a new global corporate reputation measurement scale with sound psychometric properties. This scale will be adapted for click and mortars and pure players. This paper unlocks future perspectives by suggesting a causal model that integrates online corporate reputation and its main antecedents and consequences.

Practical implications

From a managerial perspective, this research offers insights to managers with the main orientations surrounding the components of global corporate reputation. Moreover, the AOL mappings delineate which quadrants the managers would like to be fitted into or avoid, and hence define more precisely which key elements should be stressed or discarded.

Originality/value

This research outlines AOL, an original qualitative projective technique that can be used to understand customers’ thoughts, which are stocked and collected as images. Moreover, this research intends to analyze the gathered data using both INDSCAL and fuzzy k-means cluster analysis to reduce conventional biases related to subjectivity.

Details

Qualitative Market Research: An International Journal, vol. 27 no. 1
Type: Research Article
ISSN: 1352-2752

Keywords

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