Denim Kumaşlar için Abclass-Miner Sınıflandırma Algoritması

Year 2024, Volume: 37 Issue: 1, 326 - 337, 01.03.2024

Gözde Katırcıoğlu Emel Kızılkaya Aydogan Esra Akgul Yılmaz Delice

https://doi.org/10.35378/gujs.1185130

Abstract

Hızla gelişen bilişim teknolojileri (BT) ile büyük miktarda verinin elde edilmesi ve saklanması kolaylaşmıştır. Ancak üretilen ve kaydedilen tek başına bir anlam ifade etmeyen veriler ancak belirli bir amaç için işlendiğinde anlamlı hale gelmektedir. Ham veriyi anlamlı bilgiye dönüştürmek veri madenciliği ile yapılabilmektedir. Bu çalışmada, denim kumaş üretim parametrelerine göre denim kumaş kalite özelliklerinin sınıflandırılması ve analizi yapılmıştır. Bu çalışma, yeni bir sınıflandırma kuralı çıkarım algoritması önermektedir. Önerilen algoritma, esas olarak Yapay Arı Kolonisi Optimizasyonu (ABC) olarak bilinen bir sürü zekası meta-sezgiseline dayanmaktadır. Algoritmanın her adımında görevli arı aşaması ve gözcü arı aşaması olarak adlandırılan iki aşama vardır. Bu algoritma ilgili literatürdeki sınıflandırma algoritmaları ile karşılaştırılmıştır. Önerilen bu algoritma, çeşitli meta-sezgisel ve sinir ağlarını akıllıca birleştiren ve sınıflandırma kuralları oluşturabilen yeni bir veri madenciliği aracıdır. Elde edilen sonuçlar, geliştirilen veri madenciliği yaklaşımlarının denim kumaş üretiminde ağırlık ve genişlik analizinde oldukça faydalı olabileceğini göstermektedir.

Keywords

Veri madenciliği, Yapay Arı Kolonisi, Sınıflandırma Algoritması, Denim Kumaş

Supporting Institution

TÜBİTAK

Project Number

TEYDEB-1505, 5200006

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

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.

Keywords

Data mining, Artificial bee colony, Classification algorithm, Denim fabrics

Project Number

TEYDEB-1505, 5200006

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