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Rule based Classification of Graph Theory Concepts by Use Case Analysis
Objective: Nature of the problem is always solvable, partially solvable or unsolvable but by using certain techniques we may resolve uncertainty up to some extent. This study evaluates Graph Theory (GT) concepts, in order to resolve its complexity by applying use case analysis method and helps to classify them. Methods/Statistical Analysis: Experiment has been formulated on 38 GT concepts. For each GT concept identification of use cases and its corresponding activities is performed. Further, proposed method helps in classifying the problem. Findings: In this paper rule based random sampling technique for use case analysis is being proposed. It helps to compute required number of use cases for solving graph theory related problems and to categorize them into simple, moderate or complex classes. In order to achieve this, proposed work deals with identifying use cases, activities in each use case, classification of activities in terms of simple, moderate and complex classes. Novelty/Improvement: Computation of problem length (PL) through proposed rule based random sampling helps in classification of problem. Classifying the problem helps to reduce its complexity. Proposed classification method/process achieves the same.
Graph Theory (GT) Concepts, Rule Based Random Sampling, Use Cases.
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