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International Journal of Enterprise Modelling
Published by International Enterprise Integration Association
ISSN : 16939220     EISSN : 29878713     DOI : https://doi.org/10.35335/emod
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation
The International Journal of Enterprise Modelling serves as a venue for anyone interested in business and management modelling. It investigates the conceptual forerunners and theoretical underpinnings that lead to research modelling procedures that inform research and practice.
Arjuna Subject : Matematika - Matematika Terapan
Articles 5 Documents
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Search results for , issue "Vol. 14 No. 3 (2020): Sep: Enterprise Modelling" : 5 Documents clear
Hybrid Grid Partitioning, Rough Set Theory, and Fuzzy Rule Generation for Enhanced Association Rule Mining on Complex Datasets Neeta Grant Caribbean
International Journal of Enterprise Modelling Vol. 14 No. 3 (2020): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v14i3.33

Abstract

Association rule mining plays a crucial role in extracting valuable insights and patterns from complex datasets. However, traditional association rule mining algorithms often face challenges in accurately discovering relevant rules due to the complexity, uncertainty, and vagueness inherent in such datasets. In this research, we propose an integrated approach that combines hybrid grid partitioning, rough set theory, and fuzzy rule generation to enhance association rule mining on complex datasets. First, hybrid grid partitioning is employed to divide the data space into a set of refined grid cells, allowing for more precise representation of the dataset's structure. Next, rough set theory is utilized to handle uncertainty and vagueness by computing lower and upper approximations of concepts. This enables the identification of objects that share similar condition attribute values and improves the robustness of rule generation. Additionally, fuzzy rule generation is incorporated to capture nuanced relationships and patterns within the dataset. Fuzzy logic is employed to represent imprecise and subjective concepts, facilitating the discovery of deeper insights and enhancing the comprehensibility of the generated rules. The proposed approach contributes to the accuracy, interpretability, and relevance of association rules in complex datasets. By integrating multiple techniques, it addresses the limitations of traditional algorithms and provides a comprehensive framework for knowledge discovery. Experimental evaluations demonstrate the effectiveness of the proposed approach in enhancing rule discovery accuracy and interpretability compared to traditional methods. Although some limitations, such as scalability and parameter sensitivity, need to be addressed, the research's findings highlight the potential of the integrated approach for extracting valuable insights from complex datasets. The proposed methodology has broad applicability across various domains and can empower decision-making processes in areas such as market basket analysis, customer behavior analysis, bioinformatics, and web mining. Future research can focus on addressing the identified limitations and further validating the approach's effectiveness in real-world scenarios.
Complex Data Set Problem Solving With Hybrid Grid Partition And Rought Set Method For Fuzzy Rule Generation As A Solution Mikstas Romantė Serksnas; Bučienė Naujienos Leganovic
International Journal of Enterprise Modelling Vol. 14 No. 3 (2020): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v14i3.34

Abstract

This research proposes a novel approach that combines hybrid grid partitioning and the rough set method for fuzzy rule generation to address the challenges posed by complex data set problem-solving. The approach aims to improve data representation, handle uncertainty, identify relevant features, extract dependency rules, and generate accurate fuzzy rules. The hybrid grid partitioning technique probabilistically assigns data points to cells based on local density, enhancing data representation and capturing variations in data density. The rough set method is applied within each cell to identify relevant features and extract dependency rules, considering the uncertainty and incompleteness present in the data. Fuzzy logic is incorporated to generate linguistically interpretable fuzzy rules that capture the complex relationships within the data set. The proposed approach offers an effective solution for complex data analysis, enabling enhanced decision-making, prediction accuracy, and understanding across various domains. However, the approach has limitations, including sensitivity to parameter selection, computational complexity, assumption of independence, interpretability of fuzzy rules, and generalizability to diverse domains. Further research and refinement are necessary to address these limitations and enhance the approach's performance and applicability. Overall, this research contributes to the field of complex data analysis by providing a comprehensive approach for problem-solving, with the potential to advance decision-making and understanding in complex data sets.
An Enhanced Hybrid Grid Partitioning and Rough Set Approach for Fuzzy Rule Generation in Complex Dataset Problem Szolga Mihaela Munteanu; Makeithappen Daniela Ioan
International Journal of Enterprise Modelling Vol. 14 No. 3 (2020): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v14i3.35

Abstract

This research presents an enhanced hybrid grid partitioning and rough set approach for fuzzy rule generation in complex datasets. Complex datasets pose challenges due to their high dimensionality, nonlinearity, and uncertainty, which traditional analysis techniques struggle to address. Fuzzy rule-based systems offer a promising solution, but generating effective fuzzy rules becomes increasingly difficult as the dataset complexity increases. To overcome these challenges, we propose a methodology that integrates hybrid grid partitioning and rough set theory. The hybrid partitioning technique captures global and local patterns, while rough set theory handles uncertainty and incomplete information. The approach generates accurate and interpretable fuzzy rules by optimizing the rule set's size and balancing accuracy and interpretability. Experimental evaluations demonstrate the approach's effectiveness in terms of rule accuracy, interpretability, and computational efficiency. The generated fuzzy rules provide valuable insights into complex relationships, aiding decision-making processes in various domains. The research contributes to the advancement of fuzzy rule generation techniques for complex datasets and offers a practical solution for knowledge extraction in complex systems.
Hybrid Grid Partition and Rought Set Method for Fuzzy Rule Generation in Production Planning Problem Approach Christos Kossyva Broadbent; Tsekos Rokkas Plattner
International Journal of Enterprise Modelling Vol. 14 No. 3 (2020): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v14i3.36

Abstract

This research introduces a hybrid grid partition and rough set method for fuzzy rule generation in the production planning problem. The approach combines fuzzy logic, rough set theory, and grid partitioning techniques to address the complexity and uncertainty inherent in production planning decisions. By integrating these techniques, the approach captures the relationships between input variables and production planning decisions, allowing for informed decision-making in dynamic manufacturing environments. The research demonstrates the generation of high-quality fuzzy rules based on membership functions, grid partitioning, and attribute reduction. A numerical example is provided to illustrate the application of the approach, showcasing its potential in improving decision-making accuracy in production planning. However, the research acknowledges limitations such as the simplified scenario, assumption of variable independence, and scalability concerns. Further research is necessary to address these limitations and validate the approach in more realistic and complex production planning scenarios. Overall, the hybrid grid partition and rough set method for fuzzy rule generation offer a promising approach to enhance decision support systems in production planning and contribute to the advancement of manufacturing and supply chain management.
Improving Production Planning Decision-Making with Hybrid Grid Partitioning and Rough Set Method for Fuzzy Rule Generation Georgopoulos Panagiotis Hatsopoulos; Dunnill Cigalas Diakoulakis; Pekka Cromar Kayamba
International Journal of Enterprise Modelling Vol. 14 No. 3 (2020): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v14i3.37

Abstract

This research proposes a hybrid decision-making framework that combines grid partitioning, rough set method, and fuzzy logic to enhance production planning decision-making. The framework aims to address the complexities and uncertainties associated with production planning processes and provide a structured approach for deriving optimal decisions. The research utilizes a numerical example to illustrate the application of the hybrid framework, where fuzzy rules are generated based on input variables, grid partitioning is employed to discretize the input space, and fuzzy reasoning is utilized to determine the optimal production quantity. The findings highlight the potential of the hybrid approach in improving decision-making outcomes in production planning scenarios. However, limitations such as simplified assumptions, limited scope of validation, and the need for further empirical validation are acknowledged. The research contributes to the scientific understanding of decision support systems in production planning and emphasizes the importance of practical implementation and validation in real-world contexts. Future research can explore the limitations, validate the proposed framework in diverse scenarios, and conduct comparative analyses with existing approaches.

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