Using data mining technique to explore anthropometric data towards the development of sizing system

Zakaria, Nor Balkish; Mohd, Jamil Salleh; Taib, Norrulhuda Mohd; Tan, Yong Yuan; Wah, Yap Bee

doi:10.1109/itsim.2008.4631721

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Trees are pruned to their most obvious size, and then, a pruning step is consistently implemented to manage the tree's constraint to sum up to covered data. These learning models apply either bagging (also known as bootstrap aggregating) boosting [18][19][20][21][22]. Sacking is an inspecting AI meta-calculation used to further develop learning models' exhibition.…”

Section: Decision Treementioning

confidence: 99%

Research Design of Fashion Elements Identification of Clothing Based on Decision Tree Algorithm and IoT

Wang

2022

Wireless Communications and Mobile Computing

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Attributable to challenges of social event enormous volumes of material area information in a setting of less mining research, foreseeing the qualities of articles of clothing turns into a significant open issue which gets increasingly more consideration from the materials research local area. In this exploration work, the field of data mining endeavors to anticipate clothing insurance factors completely plan on understanding the computational individual of learning. Qualities of dress learning are being explored as a methodology for settling on the decision and utilization of getting ready data and their outcomes. It is seen from the results got by experimentation that the linear regression hushes up engaging as a result of adequacy as far as high forecast rate and linear regression can find the dress protection execution in a most proficient way in contrast with any remaining inclining calculations tested.

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Section: Decision Treementioning

confidence: 99%

Research Design of Fashion Elements Identification of Clothing Based on Decision Tree Algorithm and IoT

Wang

2022

Wireless Communications and Mobile Computing

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“…There are a few sub-areas of research within this that are highly focused, including finding the most relevant body measurements to develop a new sizing system [13][14][15] and evaluating the fit of the garment using virtual try-on [16,17]. N. Zakaria et al [18] employed principal component analysis, k-means clustering, and regression tree to address issues related to the identification of the most important body measurements. Similarly, Hsu and Wang [19] used Kaiser's eigenvalue criteria along with the Classification and Regression Trees (CART) decision tree algorithm to identify and classify significant patterns in the body data.…”

Section: Research Backgroundmentioning

confidence: 99%

Garment Categorization Using Data Mining Techniques

Jain

Kumar

2020

Symmetry

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The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

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“…Itsmain objective is to divide the data points into 'K' partitions. Zakaria et al, [14], Bagherzadeh et al, [15] and Elfaki and Ali [16] used principal component analysis to determine key anthropometric measurements and cluster analysis for the sizing system. Rao et al [17] clustered 10096 anthropometric datasets of children in 54 districts of Uttar Pradesh into 4 clusters according to their average height and weight.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Fuzzy Clustering Methodology for Garment Sizing

Opaleye¹,

Kolawole²,

Charles-Owaba³

2019

AJDMKD

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With the growing demand for Ready-To-Wear outfits especially in African textile prints, the currently used European, American and Asian garment sizing systems seems unsuitable for the Nigerian garment industry where customer's choose clothing item not only due to fit in terms of body measurements but also the dress culture, style, preference and some other implicit requirements. This study aims to develop a size chart for different styles of trousers worn by Nigeria male population. Anthropometric data of 500 customers were taken in a natural random process and from stable tailoring establishments. The data was analysed using descriptive statistics and the fuzzy clustering methodology (FCM) was used as a suggestive approach which describes subjectivity in customer preferences. Analysis of the FCM output shows that the number of individual measurements with misfit has no significant difference (F estimated = 1.119, p-value=0.375 and F critical = 2.866) across cluster. The percentages of misfit were 38.0, 23.4, 31.6, 31.4 and 3.8% for hip measurement, length, waist, thigh and bottomgirth respectively. The developed sizing system which reflects subjectivity in customer's selection of trouser may also enhance both producer and retailer's production and replenishment policy.

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Using data mining technique to explore anthropometric data towards the development of sizing system

Cited by 7 publications

References 17 publications

Research Design of Fashion Elements Identification of Clothing Based on Decision Tree Algorithm and IoT

Research Design of Fashion Elements Identification of Clothing Based on Decision Tree Algorithm and IoT

Garment Categorization Using Data Mining Techniques

Application of Fuzzy Clustering Methodology for Garment Sizing

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