The Application of Data Mining in FFE of the Fashion Product Development

CHEN Xiaopeng, LUO Yun, ZHU Fanglong School of Clothing

Zhongyuan University of Technology Zhengzhou, China

e-mail: chenxiaopeng@zzti.edu.cn

Abstract—Aiming at providing support for fashion product development, the research analysed the Fuzzy Front End in fashion design and the processes of the pre-development in the industry. The functions of data mining and its applications in fashion product development were discussed. A case study was conducted based on the data mining of the 3-D body scan measurements. Principal component analysis, correlation analysis, and the optimal bandwidth selection in kernel density estimation were employed to establish the specification of pants for target consumers. It is concluded that the data mining is an important tool to transform the data into information helpful in making development decision and solving technology problems.

Keywords-Product development, Fuzzy Front End, Data mining

I. INTRODUCTION The process of product innovation involves the

introduction of a good or service that is new or improved, including market research and analysis, the idea generation, product design and detail engineering[1].

The key of fashion product innovation management lies in the precise understanding of the consumers and proper positioning of the product. These are all conducted at the first stage in generating and commercializing new products. The Fuzzy Front End is used to describe the starting period of new product development processes. It is the phase between first consideration of an opportunity and entering the structured development process[2]. The Fuzzy Front End consumes 50% of development time and includes all activities from searching for new opportunities to the development of a complete concept[3].

In fashion product development, predevelopment activities include market assessment, technical assessment, supply chain assessment, market research, product concept testing, value to the customer assessment, product definition，business and financial analysis. These activities yield vital information to make a development decision.

II. THE APPLICATION OF DATA MINING Data mining is the process of extracting patterns from

data. Data mining is becoming an increasingly important tool to transform these data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance and scientific discovery[4].

A. The Function of Data Mining The primary function of data mining is to assist in the

analysis of collections of observations of behaviour. Knowledge Discovery in Databases is used to describe the process of finding interesting, useful data[5].

Data mining commonly involves five classes of tasks: • Classification: to arrange the data into predefined

groups. Common algorithms include Decision Tree Learning, Nearest neighbour, naive Bayesian classification and Neural network.

• Clustering: to classify the groups while the groups are not predefined. The algorithm should try to group similar items together.

• Regression: to find a function which models the data with the least error.

• Association rule learning: to searches for relationships between variables.

• Predictive analytic: to exploit patterns found in historical and transaction data to identify risks and opportunities, and analyse current and historical facts to make predictions about future events[6].

B. The Application of Data Mining Data mining can be used to uncover patterns. The

increasing power of computer technology has increased data collection and storage. Automatic data processing has been aided by computer science, such as neural networks, clustering, genetic algorithms, decision trees and support vector machines. Data mining is the process of applying these methods to the intention of uncovering hidden patterns[7]. It has been used for many years by businesses, scientists to sift through volumes of data. The application of data mining in fashion design could be summarized in Fig. 1.

Figure 1. The application of data mining in fashion design.

2010 International Symposium on Computational Intelligence and Design

978-0-7695-4198-3/10 $26.00 © 2010 IEEE

DOI 10.1109/ISCID.2010.71

255

2010 International Symposium on Computational Intelligence and Design

978-0-7695-4198-3/10 $26.00 © 2010 IEEE

DOI 10.1109/ISCID.2010.71

255

2010 International Symposium on Computational Intelligence and Design

978-0-7695-4198-3/10 $26.00 © 2010 IEEE

DOI 10.1109/ISCID.2010.71

255

2010 International Symposium on Computational Intelligence and Design

978-0-7695-4198-3/10 $26.00 © 2010 IEEE

DOI 10.1109/ISCID.2010.71

255

2010 International Symposium on Computational Intelligence and Design

978-0-7695-4198-3/10 $26.00 © 2010 IEEE

DOI 10.1109/ISCID.2010.71

215

TABLE I. TOTAL VARIANCE EXPLAINED

Component Initial Eigenvalues Extraction Sums of Squared

Loadings Rotation Sums of Squared

Loadings

Total % of Variance Cumulative % Total % of

Variance Cumulative % Total % of Variance Cumulative %

1 6.058 46.603 46.603 6.058 46.603 46.603 4.347 33.439 33.439 2 2.625 20.192 66.795 2.625 20.192 66.795 3.599 27.683 61.123 3 1.505 11.579 78.374 1.505 11.579 78.374 2.243 17.252 78.374 4 0.928 7.139 85.514 5 0.558 4.293 89.807

… … … … … … … … … … Extraction Method: Principal Component Analysis

For example, data clustering can discover the segments or groups within a customer data set. Another example is that the market basket analysis could identify the characteristics of the customers' favourite items from a clothing store records.

Also data mining is a highly effective tool in the catalog fashion marketing. Data mining tools can identify patterns of customers and help identify the most likely customers.

III. CASE STUDY: SPECIFICATION DESIGN OF PANTS

A. Body Scanning Data for Controlling Measurements Due to the lack of specification research on pants, the

controlling measurements in the size system are not sufficient for pattern-making of the pants. The product development in pants has lots of fitting problems. To counter the fitness demand of pants product, 3-D body data was collected and the association rule was analysed.

To get a precise size system for niche market of young woman's pants, the samples were selected among the target consumers. The 3-D body data of 227 young woman (21-25 years old) were collected.

Factor analysis on the 13 measurements of the pants is shown in the Table I.

From the Table I, the top 3 components are extracted as the principal component. From the Rotated component Matrix (Table II), the meaning of the three principal components are clear.

TABLE II. ROTATED COMPONENT MATRIX

Component 1 2 3

1 Hip girth 0.904 0.220 0.156 2 Leg girth 0.886 0.174 0.130 3 Abdomen girth 0.857 0.144 4 Knee girth 0.836 0.142 0.107 5 Waist girth 0.835 0.169 -0.119 6 Ankle girth 0.314 0.180 7 In-length 0.983 8 Out-length 0.154 0.934 0.263 9 Total height 0.307 0.908 10 Waist -knee length 0.815 0.439 11 Height of crotch 0.309 0.168 0.857 12 Waist-hip length 0.184 0.792 13 Total crotch length 0.533 0.261 0.703

According to their functions in pants structure, the components are named:

• Circumstance components: including hip girth, leg girth, abdomen girth, knee girth, waist girth and ankle girth;

• Length components: including in-length, out-length, total height and waist-knee length;

• Crotch components: including height of crotch, waist-hip length and total crotch length.

B. Correlation Analysis Standard deviation shows how much variation it is from

the "average" [8]. It helps detect tampering of data. A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data are spread out over a large range of values.

From Table III, the hip girth highly correlated with other girthes except the ankle girth. From Table IV, the lengthes of the sections of the pants could be calculated from the total height because these components are all in high correlation with total height. From the Table V, the height of crotch correlated with the total crotch length, which is the most complained area of the pants by the consumers.

TABLE III. CIRCUMSTANCES COMPONENTS VARIABLES CORRELATION ANALYSIS

Hip girth

Leg girth

Abdomen girth

Knee girth

Waist girth

Ankle girth

Hip girth 1.000 0.905 0.781 0.747 0.767 0.228 Leg girth 0.905 1.000 0.732 0.768 0.677 0.158 Abdomen girth 0.781 0.732 1.000 0.647 0.676 0.263 Knee girth 0.747 0.768 0.647 1.000 0.621 0.284 Waist girth 0.767 0.677 0.676 0.621 1.000 0.204 Ankle girth 0.228 0.158 0.263 0.284 0.204 1.000

TABLE IV. LENGTH COMPONENTS VARIABLES CORRELATION ANALYSIS

Total height Out-length In-length Total height 1.000 0.899 0.894 Out-length 0.899 1.000 0.947 In-length 0.894 0.947 1.000 Waist -knee length 0.756 0.854 0.779

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TABLE V. CROTCH COMPONENTS VARIABLES CORRELATION ANALYSIS

Height of crotch

Total crotch length

Waist-hip length

Height of crotch 1.000 0.837 0.536 Total crotch length 0.837 1.000 0.411 Waist-hip length 0.536 0.411 1.000

Thus the hip girth, the total height and the height of

crotch were defined as the controlling measurements for pants product of woman.

C. Grading Margin The histogram could be used to estimate the density

distribution of the basic measurements[8]. When the grading margin is too large, the differences of the body data are eliminated, which might cause the fitting problem. On the contrary, the small grading margin get better matching to the curve of density distribution, but the amount of the patterns lead to efficient problems in the operation of the factories and the tiny disparity in specifications has little influence on clothing. Rational grading margin is the key elements in the feasibility of the size system.

The research adopted the optimal bandwidth selection in kernel density estimation (formula 1) to calculate the grading margin of the sections.

1/( 4) 1/( 4)4ˆ ( )2

d dj jh n

dσ+ − +=

+ (1)

D: the dimensions of the samples; d=1 for each section; N: the amount of the samples; Σj: the standard deviation of the j section. The result of the standard deviation was shown in Fig. 2.

In this case, the optimal grading margin is: 1.48cm for hip girth, 1.98cm for the total height and 0.45 cm for the height of crotch.

Figure 2. The Standard Deviation Curve of the Principal Component

Figure 3. The Product Development of Fashion Product

IV. CONCLUSION It is in the Fuzzy Front End that major commitments are

typically made involving time, money, and the product’s nature to set the course for the entire project and final end product. The process of the pre-development in fashion industry could be demonstrated in Fig. 3.

Data mining provides support in market research and analysis, the idea generation, product design and detail engineering. It is an important tool to transform the data into information helpful in making development decision as well as in solving technology problems in fashion design.

ACKNOWLEDGMENT This research is part of the project: Dynamics and Structure

Research Based on Remote 3-D Body Scanning and its Application in Functional Clothing (No. 102102210255), funded by Henan Provincial Science and Technology Office, whose support is gratefully acknowledged.

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