International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 17, No. 6, 8 Dec. 2025
Cover page and Table of Contents: PDF (size: 1632KB)
Dynamic Data Mining: Using Dynamic ID3 Algorithm to Solve Any Problem that Needs Decision Tree Support
PDF (1632KB), PP.127-145
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Author(s)
Amir Amjad Gharbi
1,*
Bassel Alkhatib
1,2
Index Terms
Dynamic Data Mining, Decision Tree ID3, Algorithm Parameters, Stored Dataset Criteria, Performance Measures
Abstract
Most programmers and users resort to find individual solution per problem depending on the data and nature of problem, that will lead to solve a specific problem using an algorithm without the ability of this solution to solve a new problem. This variance comes from the difference in algorithm parameters from one problem to another, as these parameters related to data nature, its size, and values it carried that can affect the way algorithm work. Individual solutions lead to increase in time cost and effort spent on solving a new problem, which the new problem requires to work on programming new criteria for algorithm solution. That is prompted us to highlight necessaries to develop main components for algorithms used in practical life, such as data mining algorithms so that a solution designed for one problem can be more easily adapted to new problems with different data structures, within the general scope of decision tree applicability. These algorithm components need control mechanism settings, so when using component to solve problem, there is no need to develop algorithm settings again, regardless data size and data structure. We found that the dynamic solution saves effort and time needed to solve problems with same algorithm. In this paper, we present our methodology for using ID3 decision tree algorithm to mine data dynamically, and the mechanism used to achieve the dynamic solution, that provides a flexible and reusable solution for a wide range of problems that require decision tree support, reducing the need to redesign or reimplement models for each new task The proposed model was tested on three datasets. The proposed model achieved an accuracy of 97%, 97%, and 93% on the breast cancer, heart disease, and diabetes datasets, respectively.
Cite This Paper
Amir Amjad Gharbi, Bassel Alkhatib, "Dynamic Data Mining: Using Dynamic ID3 Algorithm to Solve Any Problem that Needs Decision Tree Support", International Journal of Modern Education and Computer Science(IJMECS), Vol.17, No.6, pp. 127-145, 2025. DOI:10.5815/ijmecs.2025.06.09
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