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Efficient Big Data Classification Using a Fuzzy Rule-Based Granularity Model
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AI
DOI：10.5391/IJFIS.2025.25.4.500.png)
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Abstract
En 中文
The effective management and utilization of big data are becoming increasingly important for organizations as it holds the potential to provide valuable insights that can inform decisionmaking in various domains. The rapidly changing technology landscape and the demand for real-time data processing present challenges for organizations to effectively store, manage, and analyze big data while ensuring data security and privacy. To address these challenges, there is a need for interdisciplinary collaboration between information technology, data science, and businesses to develop new tools, techniques, and methodologies for big data management and analysis. The ultimate goal is to develop big data into an asset that drives organizational success. This study focuses on determining the most effective methods and techniques for managing and analyzing big data and improving real-time data processing. A fuzzy rule-based classification model is proposed and evaluated on a CPU benchmark v4.CSV dataset. The results show high precision and recall values, indicating the effectiveness of the model. The model has the advantage of interpretability and the potential for high accuracy if the rules are well-defined and pertinent to the data.
Keywords:
Big data
Classification
Fuzzy fule
Granularity model
Journal
I
IF:
1.2
Papers:
20
Citations:
0