Developed ABCLASS-Miner Classification Algorithm Based Rule Extraction for Denim Fabrics


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Katircioglu G., KIZILKAYA AYDOĞAN E., AKGÜL E., DELİCE Y.

GAZI UNIVERSITY JOURNAL OF SCIENCE, vol.37, no.1, pp.326-337, 2024 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 1
  • Publication Date: 2024
  • Doi Number: 10.35378/gujs.1185130
  • Journal Name: GAZI UNIVERSITY JOURNAL OF SCIENCE
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Academic Search Premier, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Metadex, Civil Engineering Abstracts, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.326-337
  • Erciyes University Affiliated: Yes

Abstract

Obtaining and storing large amounts of data have become easier with the rapidly developing information technologies (IT). However, the data generated and collected, which are irrelevant in and of themselves, become useful only when they are analyzed for a specific reason. Data mining may transform raw data into useful information. In the present study, classification and analysis of denim fabric quality characteristics according to denim fabric production parameters were carried out. The present study proposes a new classification rule inference algorithm. The suggested approach is mostly based on Artificial Bee Colony Optimization (ABC), a swarm intelligence meta-heuristic. In each step of the algorithm, there are two phases called the employed bee phase and the onlooker bee phase. This algorithm has been compared with the classification algorithms in the related literature. This proposed algorithm is a new data mining tool that intelligently combines various metaheuristic and neural networks and can generate classification rules. The results indicate that the proposed data mining algorithms may be highly useful in determining weight and width in denim fabric manufacture.