Discovering the relationships between yarn and fabric properties using association rule mining

Abstract

Investigation of the effects of yarn parameters on fabric quality and finding important parameters to achieve desired fabric properties are important issues for the design process with the aim to meet the needs of the textile industry and the consumer for complex and specific requirements of functionality. Despite many statistical and mathematical studies that predict and reveal specific properties of utilized yarn and fabric materials, a number of challenges continue to exist when evaluated in many perspectives, such as discovering complex relationships among material properties in data. Data mining plays an important role in discovering hidden patterns from fabric data and transforming it into knowledge. Therefore, the aim of the study is to uncover relationships between yarn parameters and fabric properties using an extended FP-Growth algorithm in association rule mining. This study extracts different types of frequent itemsets (closed, maximal, top-k, top-k closed, top-k maximal) that have not been determined in textile sector before. This article also proposes two novel concepts, closed frequent item and maximal frequent item, to identify significant items in data. In the experimental studies, the algorithm was executed on a real-world textile dataset with different support threshold values to compare the different types of patterns. Experimental results show that proposed approach is very useful for discovering rules related to yarn and fabric properties.