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

Yıldırım, Pelin; Birant, Derya; Alpyıldız, Tuba

doi:10.3906/elk-1611-16

Cited by 9 publications

(7 citation statements)

References 28 publications

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“…. ..Xm} is called Top-k frequent itemset if it includes frequent itemset of the highest support to the k th support itemset in I, where I is a set of all identical frequent itemsets (Yildirim, Birant & Alpyildiz, 2017).…”

Section: Proposed Methodologymentioning

confidence: 99%

TKFIM: Top-K frequent itemset mining technique based on equivalence classes

Iqbal

Shahid

Roman

et al. 2021

PeerJ Computer Science

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Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset’s characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent itemsets mining (TKFIM). It uses class equivalence and set-theory concepts for mining FIs. The proposed procedure does not miss any FIs; thus, accurate frequent patterns are mined. Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO). It is found that the proposed TKFIM has outperformed the results of these approaches in terms of execution and performance, achieving 92.70, 35.87, 28.53, and 81.27 percent gain on Top-k miner using Chess, Mushroom, and Connect and T1014D100K datasets, respectively. Similarly, it has achieved a performance gain of 97.14, 100, 78.10, 99.70 percent on BOMO using Chess, Mushroom, Connect, and T1014D100K datasets, respectively. Therefore, it is argued that the proposed procedure may be adopted on a large dataset for better performance.

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Section: Proposed Methodologymentioning

confidence: 99%

TKFIM: Top-K frequent itemset mining technique based on equivalence classes

Iqbal

Shahid

Roman

et al. 2021

PeerJ Computer Science

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“…17,27 Data mining can help to discover the complex relationship between yarn parameter and fabric quality. 27 But in this section, the liquid-containing fabrics are equivalent to porous media, many of whose properties need to be characterized during the drying process. These properties are obtained on the base of the parameters of fabrics and gas-liquid phases.…”

Section: Applying the Finite Element Methods To Analyze The Fabric Continuous Drying Processmentioning

confidence: 99%

Process analysis and optimization of open-width fabric continuous drying based on numerical simulation

Wang

Zhang

Chen

et al. 2020

Textile Research Journal

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As an indispensable part of textile processing, the fabric drying process has a great impact on product quality and overall energy consumption. To reveal the characteristics of the continuous drying process of various fabrics and optimize process parameters for improving productivity and saving energy, a finite element model is built to simulate the continuous fabric drying process, and an optimization method is applied to optimize process parameters based on the model. Specifically, a finite element model is first built; the model can predict distribution of water content and surface temperature of three kinds of fabric in the continuous drying process under different process parameters. The model is then verified by experiments, and the experimental results agree well with the numerical results: The mean absolute errors of distribution of water content and surface temperature of fabrics are 4.22% and 2.15℃, respectively. The numerical results indicate that wind velocity, wind temperature, and fabric velocity have a significant influence on the drying rate and surface temperature of fabrics in the continuous drying process, which, however, are not affected obviously by initial water content. It is also found that under the same initial and technological conditions, the drying rate and surface temperature of fabrics in the continuous drying process are lower than those in the intermittent drying process. Second, the Taguchi method is applied to design continuous fabric drying schemes, considering the interaction effect of technological parameters on the drying process. The numerical model is then applied to simulate these schemes, and the TOPSIS method is applied to analyze and compare these numerical results. The optimal technological parameters are determined; the optimal parameters can help to save energy by about 27.8 % and enhance energy efficiency by about 16 % in the continuous drying process. It is worth noting that the interaction effect of fabric velocity and wind temperature on the continuous drying process is more significant than their independent effects.

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“…Yildirim et al tried to determine the relationship between yarn and fabric characteristics with their association rules mining study performed on the data of a textile company. In determining the association rules, the FP-Growth algorithm was used [30].…”

Section: Literature Reviewmentioning

confidence: 99%