Informatics Publishing Limited

Gagan Kumar ¹, Neeraj Mangla ¹, Aakanksha Mahajan ²

Affiliations
1 M. M. Engineering College, Maharishi Markendshwar university, Mullana, IN
2 M. M. Engineering college, Maharishi markendshwar university, Mullana, IN
3 Doon Valley Institute of Engineering and Technology, Kurukshetra University, IN

Abstract

The problems of mining association rules in a database are introduced. Most of association rule mining approaches aim to mine association rules considering exact matches between items in transactions. A new algorithm called ―Improved Data Mining Based on Semantic Similarity to mine new Association Rules‖ which considers not only exact matches between items, but also the semantic similarity between them. Improved Data Mining (IDM) Based on Semantic Similarity to mine new Association Rules uses the concepts of an expert to represent the similarity degree between items, and proposes a new way of obtaining support and confidence for the association rules containing these items. An association rule is for ex: i.e. for a grocery store say ―30% of transactions that contain bread also contain butter; 2% of all transactions contain both of these items‖. Here 30% is called the confidence of the rule, and 2% the support of the rule and this rule is represented as Bread -> Butter. The problem is to find all association rules that satisfy user-specified minimum support and minimum confidence constraints. This paper then results that new rules bring more information about the database.

Keywords

Data Mining, Semantic Similarity, Association Rules, Support, Confidence, Fuzzy Logic.

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Gagan Kumar ¹, Vinay Chopra ²

Affiliations
1 Department of Computer Science and Engineering, IK Gujral Punjab Technical University, IN
2 Master of Computer Applications, D.A.V. Institute of Engineering and Technology, IN

Abstract

Software testing is an essential phase in software design process, accounting for more than half of the total cost due to its rigorous and time-consuming nature. Path test data generation is the most important stage in software testing, and researchers have devised several methods to automate it. In this research, a novel approach based on ant colony optimization and negative selection algorithm (NSA) is projected to automatically create test data for path testing. The most widely used benchmark programs such as triangle classification, dayfinder, minmax and isprime, has been used to test the proposed approach. When compared to random testing, the experimental findings reveal that the proposed method is more efficient in terms of coverage, execution time and more effective in terms of test data creation.

Keywords

Test Data Generation (TDG), Meta-Heuristic, Artificial Immune Algorithm, ACO, NSA, Path Coverage, Fitness Function

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