EXPLORATIVE DATA MINING FOR THE SIZING OF

POPULATION GROUPS

Isis Peña and Herna Lydia Viktor

School of IT and Engineering, University of Ottawa, Ontario, Canada

Eric Paquet

Institute for Information Technology, National Research Council of Canada, Ottawa, Ontario, Canada

Keywords: Cluster analysis, Classification, Anthropometry, Interestingness measures-based data mining.

Abstract: In the apparel industry, an important challenge is to produce garments that fit various populations well.

However, repeated studies of customers’ levels of satisfaction indicate that this is often not the case. The

following questions come to mind. What, then, are the typical body profiles of a population? Are there

significant differences between populations, and if so, which body measurements need special care when

e.g. designing garments for Italian females? Within a population, would it be possible to identify the

measurements that are of importance for different sizes and genders? Furthermore, assume that we have

access to an accurate anthropometric database. Would there be a way to guide the data mining process to

discover only those body measurements that are of the most interest for apparel designers? This paper

describes our results when addressing these questions. To this end, we explore a database, containing

anthropometric measurements and 3-D body scans, of samples of the North American, Italian and Dutch

populations. Our results show that we accurately discover the relevant subsets of body measurements,

through the use of objective interestingness measures-based feature selection and feature extraction, for the

various body sizes within each population and gender.

1 INTRODUCTION

Apparel manufacturers develop sizing systems with

the goal of satisfying consumers’ needs for apparel

that fits. Sizing is the process used to establish a size

chart of key body measurements for a range of

apparel sizes. To produce garments that fit the

population, it follows that the sizes must correspond

to real grouping. However, this is often not the case,

as indicated by the results obtained by Shofield and

LaBat (Schofield and LaBat, 2005). Their study of

forty size charts for women’s clothing showed that

the different sizes are defined using arbitrary

constant intervals between sizes, all vertical

measurements increase as the size increases and that

the differences between the principal girths are

constant for all sizes. Considering this situation, it is

easily understandable that repeated studies of the

degree of satisfaction with apparel show that

consumers’ needs are not being met. For example, a

North American study found that about 50% of

women and 62% of men cannot find satisfactorily

fitting clothes (DesMarteau, 2000). According to

Ashdown et al. (Ashdown et al., 2007) two main

issues have limited the ability of the apparel

companies to produce garments with quality fit.

First, there has been a lack of up-to-date

anthropometric data to describe the civilian

population. Second, there is a lack of information

about the principal aspects to consider when

designing garments for a variety of body sizes and

shapes.

Recent work has, to some extent, addressed these

concerns. Anthropometric surveys such as the

CAESAR

project (Rob et al. 02), SizeUSA

(SizeUSA, 2009) and SizeChina (SizeChina, 2009)

have been carried out on civilian populations. The

CAESAR

project (Robinette et al. 2002) includes

several body measurements such as waist

circumference, hip circumference, height, weight,

etc. together with 3-D body scans of each

participant. In SizeUSA (SizeUSA, 2009) subjects

were scanned in 3-D, and the body measurements

152

Peña I., Viktor H. and Paquet E. (2009).

EXPLORATIVE DATA MINING FOR THE SIZING OF POPULATION GROUPS.

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, pages 152-159

DOI: 10.5220/0002292601520159

 SciTePress

were then extracted from the 3-D scans. Similarly,

for SizeChina (SizeChina, 2009), the heads of a

large number of individuals were scanned in 3-D.

Some recent studies attempt to find the most

important aspects to be taken into account when

designing garments. Viktor et al. (Viktor et al.,

2006) finds body size groupings in a sample of the

North American male population. Veitch et al.

(Veitch et al., 2007) aim to produce a well fitting

bodice for Australian women. After selecting twelve

out of fifty-four measures and applying Principal

Component Analysis (PCA), they define thirty-six

categories: twelve sizes and three body shapes

within each size. Hsu et al. (Hsu et al., 2007)

identify three body types and thirty-eight sizes for

the female adult Taiwanese population by applying

PCA on eleven anthropometric measures.

Although the abovementioned work attempt to

address the problem of identifying the main aspects

that should be consider for the design of garments,

they only focus either on a specific body part, or on

a gender. Moreover, they do not account for the

economic factors of the data mining process.

Importantly, they do not attempt to find the subset of

body measurements with the highest utility within

this domain. That is, they fall to focus on obtaining

those measurements that would be of the most

interest when designing apparel for different sizes

within each population and gender. This paper

addresses this need for finding the optimal set of

body measurements using an interestingness

measure-based methodology.

Our goals are as follows. We aim to understand

the typical consumers’ body profile by identifying

the natural body size groups and their distinctive

characteristics. Also, we attempt to find the most

important body measurements that define each size,

and study how these measurements interrelate.

Importantly, we aim to reduce the cost of the mining

process, and the subsequent cost of apparel design,

by reducing the number of body measurements to be

used. To this end, we employ interestingness

measures to identify the minimal sets of body

measurements that are relevant for the different

sizes, within each population and gender. In this

way, we obtain reduced body measurements of high

utility, to be used to optimize apparel design.

The remaining of this paper is organized as

follows. Section 2 introduces the CAESAR

anthropometric database, which contains subjects

from North America, Italy and the Netherlands.

Section 3 explains our methodology and results

when characterizing the populations. In this section,

we describe the cluster analysis of the

anthropometric measurements. In Section 4, we

present the approach followed when reducing the

number of body measurements, through the use of

interestingness measure-based feature selection

together with feature extraction. This section also

discusses the results obtained for the various body

sizes, within each gender and population.

Furthermore, we show how we evaluate our results

against current practice. Section 5 concludes the

paper.

2 THE CAESAR

DATABASE

CAESAR

is an anthropometric database

containing up-to-date information about European

and North American civilian populations (Robinette

et al. 2002). Anthropometric data refer to a

collection of physical dimensions of a human body.

This database includes traditional anthropometric

measurements of a large number of individuals from

North America, Italy and the Netherlands. The

numbers of anthropometric measurements are forty-

four (44) for the males, and forty-five (45) for the

females, since recording the under bust

circumference is not appropriate for the male

subjects. These measurements include height,

weight, acromial height, waist circumference, thigh

circumference and foot length, amongst others,

which were recorded by domain experts.

Additionally, the shape of each person was

scanned in three dimensions using a full body

scanner. That is, a laser scanning device measured

and recorded detailed geometry of the subjects’ body

surface. The 3-D body scans were described using a

global shape-based descriptor, which is an abstract

and compact representation of the three-dimensional

shape of the corresponding body. In essence, each

scan is represented by a set of three histograms,

which constitutes a 3-D shape index or descriptor for

the human body (Paquet et al., 2000). This index

characterizes the radial and angular distribution of

the surface elements associated with a given body,

and is designed to be orientation invariant and robust

against pose variation. In our experiments, we use

these 3-D scans to visually validate our

anthropometric data mining results. To this end, we

employ the Cleopatra system, which is able to

navigate through, and retrieve similar, 3-D scans

based on their 3-D shape (Paquet et al., 2000).

Since the goal of the CAESAR

project was to

characterize the NATO countries, a stratified

sampling strategy was followed to ensure all the

groups of the population were equality represented.

Thus the population was sampled considering the

age, gender and race. However, in the sample of

EXPLORATIVE DATA MINING FOR THE SIZING OF POPULATION GROUPS

153

North America and Italy the number of subjects in

the minority groups is very small and therefore the

sample cannot be considered as representative of the

whole population belonging to these groups.

Moreover, for the Italian population there is a

substantial percentage of missing 3-D body scans.

The best sampling was obtained in the Netherlands,

where the number of subjects per strata is balanced

and the number of subjects in the minority groups is

well represented.

3 CHARACTERIZING THE

POPULATIONS

As stated earlier, one of the most important

challenges for the clothing industry is to produce

garments with quality fit. Poor fitting garments may

never be sold or customers may return them. In

order to produce better fitting garments, accurate

and up-to-date measurements need to be further

analyzed in order to be able to better characterize the

population (Ashdown et al., 2007). To address the

aforementioned issue, we aim to find the natural

body size groupings using the anthropometric

measurements and the 3-D shapes as contained in

the CAESAR

anthropometric database. From

these groups we identify size archetypes and their

most important characteristics.

All our experiments are implemented in WEKA,

a collection of machine learning algorithms for data

mining tasks (Witten and Frank, 2005). In this study

we consider the American female population as well

as the Italian and Dutch, male and female

populations. (Interested readers are referred to

(Viktor et al., 2006) for a discussion of the results

obtained when analyzing the American male

population.)

The data was first separated based on the gender

of the subjects. The resulting sets consist of 256

American females, 413 Italian males, 388 Italian

females, 567 Dutch males and 700 Dutch females.

3.1 Cluster Analysis

In order to identify the natural body size groupings,

we first apply cluster analysis techniques to the

anthropometric data. Cluster analysis is an

unsupervised learning data mining technique used to

partition a set of physical or abstract objects into

subsets or clusters based on data similarity (Han and

Kamber, 2006, Witten and Frank, 2005).

In the context of tailoring, the ideal scenario is to

cover the greatest number of people with the fewest

number of sizes (Hsu et al., 2007). Therefore, we

aim to find the minimum number of clusters that

fully characterize the population. Since three clusters

is the minimum number that makes sense from a

tailoring point of view, i.e. small, medium and large,

we start partitioning the data into three clusters.

Then, by inspecting the cluster distribution we

decide whether is worthwhile to split the clusters, as

described in (Witten and Frank, 2005). This process

is repeated until the clusters appear well-defined.

Figure 1: Cluster visualization for (a) American female

population, (b) Italian female population, (c) Dutch female

population, (d) Italian male population and (e) Dutch male

population. The y axis represent the clusters, the x axis is

the weight range.

For our cluster analysis experimentation, a

number of clustering algorithms were considered.

These included partitioning, hierarchical, density-

based, model-based and grid-based approaches. By

inspection of the cluster distribution and through the

analysis of the results using Cleopatra system, we

found that the American female population is best

characterized with five clusters. This is also the case

for the Italian and Dutch male populations, while the

Italian and Dutch female populations are better

characterized with six clusters. We also observed

that the best partition for the American female

population is achieved by the k-means algorithm,

while the best partition for the Italian and Dutch

both males and females is achieved using a density-

based algorithm with k-means components. The

clusters obtained with these algorithms are shown in

Fig. 1, where it may be observed that the clusters are

compact and well-defined.

We also visually validate the clusters produced

by using the Cleopatra system (Paquet et al., 2000),

as follows. The Cleopatra system enables us to

retrieve the 3-D body scans associated with each

subject in the CAESAR

database. Each cluster

Centroid is used as a “seed”, and we proceed to find

the n most similar bodies, in terms of 3-D shape,

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from the database. Here, n is user defined. The

similarity is measured using the Euclidian distance.

We determine whether the n nearest bodies fall

within the same anthropometric cluster. This extra

step allows us to double-check that the

anthropometric results also “make sense” for a 3-D

shape point of view.

American females

Italian females

Dutch females

Italian males

Dutch males

Figure 2: Cluster Centroids for the five populations

ranging from smallest to largest Archetypes.

Figure 2 shows the 3-D body scans of the human

subjects that correspond to these measurements,

highlighting the difference in body types of the

clusters.

We proceed by inspecting the body measurement

of the Centroids of the various populations. We

consider the mean (in cm), the standard deviation,

and the number of subjects in each cluster. By

inspecting the body measurements of the Centroids,

the cluster distribution, and through the analysis of

the results using Cleopatra, we observe that the

anthropometric clusters do, indeed, discriminate

between the different body sizes.

3.2 Analysing the Populations

We next analyze the three populations of our study,

and discuss some similarities and differences for

both the male and female populations. In our

analysis, we consider the Centroids or archetypes,

since these are representatives of the other subjects

that belong to the same cluster.

In the analysis of the male population we also

consider the results for the American male

population as presented in (Viktor et al., 2006).

When considering the data, the next observations are

worth mentioning. Even though, the three

populations are described by five sizes, these are not

comparable, per se, to one another. For example, if

we consider the Small sizes for the Americans,

Italians and the Dutch, we observe the Italians are

considerably thinner, while both the Americans and

Dutch tend to be more robust. It may be seen, also,

that the tallest population is the Dutch, while the

shortest population is the Italians. Moreover, in the

three populations, the XX-Large subjects are shorter

than the X-Large subjects. This is an important

feature to consider when designing, for example,

pants; the legs should have short lengths for the XX-

Large size. Furthermore, if we examine the

measurements and height of the American and

Dutch archetypes of the corresponding sizes, we

observe that the range of measurements are similar,

but in general the Dutch are taller, making the

Americans the most robust population.

Regarding the female population, interesting

aspects are observed. Since the number of sizes for

the Americans is five, while for the Italians and

Dutch they are six, again we cannot compare them

directly. However, we observed that the Italians are

the thinnest and shortest population. We also

observe that the Dutch X-Small subjects are more

robust and taller than the American Small; the Dutch

size Small individuals have wider chests and hips,

and are taller than the American Medium. If we

continue in this way, it follows that the Dutch XX-

Large individuals are taller and more robust than the

American XX-Large, but surprisingly, the American

XX-Large subjects are taller and more robust than

the Dutch XX-Large. Moreover, we notice some

relationships among the different sizes of the three

populations. For instance, we notice the body

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Table 1: Measurement reduction results.

Original PCA Info Gain Gain Ratio χ

Consist. SS CFS Subset

American Female PART 75.8% 79.3%(15) 82.0%(24) 77.3%(11) 80.9%(27) 82.4% (8) 79.7%(25)

Ripper 76.6% 75.0% (10) 80.5% (19) 79.3% (19) 81.3% (12) 77.7% (13) 78.1% (25)

C4.5 76.2% 78.1% (5) 80.1% (6) 78.5% (7) 80.1% (8) 83.6% (8) 77.7% (21)

American Male PART 78.7% 76.8% (10) 82.6% (8) 81.2% (5) 82.6% (8) 83.1% (8) 80.0% (26)

Ripper 79.2% 76.3% (11) 82.9% (6) 81.2% (8) 82.9% (15) 79.7% (8) 78.7% (26)

C4.5 80.1% 74.6% (11) 83.8% (6) 81.9% (7) 83.1% (7) 84.5% (8) 79.5% (26)

Dutch Female PART 81.1% 80.4% (19) 83.9% (12) 82.3% (13) 84.7% (7) 83.1% (7) 81.9% (35)

Ripper 77.9% 77.3% (19) 83.4% (13) 82.9% (17) 82.7% (7) 81.3% (7) 81.9% (25)

C4.5 77.4% 77.4% (19) 82.9% (13) 83.3% (11) 82.7% (7) 81.7% (7) 79.3% (25)

Dutch Male PART 80.3% 82.9% (12) 82.7% (13) 82.2% (14) 81.1% (14) 80.3% (12) 81.3% (25)

Ripper 80.6% 81.5% (7) 81.3% (9) 82.4% (14) 81.3% (13) 82.0% (8) 80.3% (25)

C4.5 78.5% 82.0% (7) 81.8% (9) 82.2% (6) 80.6% (13) 80.4% (12) 80.3% (31)

Italian Female PART 78.1% 78.4% (18) 83.8% (7) 83.3% (10) 81.7% (7) 81.2% (12) 80.2% (24)

Ripper 75.8% 75.5% (7) 81.7% (7) 81.4% (7) 82.5% (8) 80.2% (8) 78.1% (31)

C4.5 76.8% 76.8% (12) 81.7% (5) 81.7% (8) 83.0% (6) 79.1% (12) 79.9% (24)

Italian Male PART 73.6% 80.9% (13) 82.1% (18) 82.1% (15) 81.1% (12) 79.7% (20) 78.7% (25)

Ripper 74.3% 83.3% (13) 78.5% (15) 80.2% (10) 77.5% (14) 78.9% (20) 78.0% (25)

C4.5 73.9% 81.4% (12) 77.0% (9) 78.2% (8) 76.3% (8) 75.8% (20) 76.3% (25)

measurements that correspond to the Dutch Large,

American X-Large and Italian XX-Large sizes are

very similar. Furthermore, the size Small of the

Dutch is comparable to the X-Large size of the

Italians. The Dutch seems to be larger than the

Americans for the smaller sizes, but the American

XX-Large resulted to be the tallest and most robust.

4 MEASUREMENT REDUCTION

Utility-based data mining accounts for the economic

aspects that impact the mining process, and aims to

maximize the utility of the process (Zadrozny et al.,

2006). In the previous section, we used in our

analysis the total number of body measurements.

Here, we aim to identify the measurements that

require special attention when designing garments.

Reducing the number of measurements aids to

increases the learning process efficiency, enhances

comprehensibility, and improves the learning

performance (Han and Kamber, 2006). To this end,

we perform two kinds of dimension reduction

techniques, namely feature selection and feature

extraction.

Interestingness measures are used in feature

selection to remove the attributes with little or no

predictive information (Kim et al., 2003, Geng and

Hamilton, 2006, McGarry, 2005). In our case study,

this means that we use interestingness measures to

identify the subset of the body measurements, which

is of most importance when describing an

Archetype. For feature selection we thus apply

Information Gain, Gain Ratio, Chi Squared, the

Consistency subset evaluator and the CFS subset

evaluator. These are measures have been widely

used in the context of feature selection and have

been found to produce good results (Cunningham,

2007). For feature extraction we use Principal

Component Analysis (PCA), a well-known feature

extraction method.

4.1 Results Obtained

In order to perform feature selection and feature

extraction, we first constructed a number of

classifiers, where the clusters we discovered during

the characterization phase acted as class labels. For

our experimentation, we consider three different

classifiers, namely RIPPER, C4.5 and PART.

The results of applying PCA and feature

selection on the anthropometric data for the

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American, Italian and Dutch male and female

populations are summarized in Table 1. Shown are

the predictive accuracy and, in parenthesis, the

number of attributes in the subset.

For the American male population we observe

that the subsets produced by Information gain, Gain

Ratio, Chi Squared and Consistency subset evaluator

considerably improve the predicted accuracy with

less body measurements. This is especially evident

for the subset produced by the Consistency subset

evaluator, where the accuracy is higher than 83%,

when using PART and C4.5. This subset then

contains the eight most important measures to define

the body sizes for the male population.

When considering the American females, it may

be seen that the subsets obtained using Information

gain, Chi Squared and two subsets produced using

the Consistency measure significantly improve the

predicted accuracy. We also observe that the subsets

produced using the Consistency subset evaluator

contains, in average, a smaller number of body

measurements than the subsets produced using

Information gain and Chi Squared measures.

Moreover, the accuracy is maximized using one of

the subsets that contain only eight body

measurements.

We then select this subset as containing the most

significant body measurements to define the body

sizes for the American females. The reduced sets of

body measurements for the American population are

shown in Table 2.

The reduced set of measurements indicate that,

for the American males, the most important body

measurements are the acromial height and knee

height together with the length of the arm. Special

attention should be paid to the knee and acromial

heights when designing long or short pants, in order

to thus take the position of the knee into

consideration. Furthermore, the length of the arm is

important when designing shirts that fit this

population well. For the American females, the

circumference under the bust and the buttock knee

length become crucial when defining the body size.

Hence, when designing clothes for the American

females, the circumference under the bust should

receive more attention than other measurements that

are mainly used in garment design, such as the bust

circumference. Moreover, the subscapular skinfold,

a measurement of subcutaneous fat accumulation, is

considered in the reduced set of body measurements

for the American females. This confirms our

previous results that the Americans are the most

robust population.

We consider, next, the results of the Dutch

population. For the Dutch males, PCA and all

feature selection methods produce good results.

Table 2: Reduced Set of Anthropometric Body

Measurements for the American Population.

Males Females

Acromial Height Sitting Arm Length (Shoulder-Wrist)

Arm Length (Shoulder-Wrist) Arm Length (Shoulder-Elbow)

Arm Length (Spine-Wrist) Bust Circumference under Bust

Hand Length Buttock Knee Length

Knee Height Sitting Stature

Stature Subscapular Skinfold

Thumb Tip Reach Thumb Tip Reach

Weight Weight

Table 3: Reduced Set of Anthropometric Body

Measurements for the Dutch Population.

Males Females

Chest Girth at Scye Arm Length (Spine-Wrist)

Hip Breadth Sitting Bust Circumference

Stature Chest Girth at Scye

Vertical Trunk Circum. Stature

Waist Circumference Thumb Tip Reach

Weight Vertical Trunk Circumference

Weight

We observe that, in general; the highest accuracy is

achieved using the subsets produced by Gain Ratio

and PCA. Although PCA produces accurate results,

its application in a tailoring scenario presents

additional challenges, because PCA do not produce

a subset of the original attributes. Instead, PCA

produces a linear combination of the original set of

attributes, preventing the direct application of PCA

results in the tailoring process. We therefore select

the subset containing six attributes produced by Gain

Ratio, because this produces the best trade-off

between accuracy and the number of attributes.

When analyzing the results for the Dutch females,

the best results are obtained using Information Gain,

Gain Ratio and Chi Squared. These three

interestingness measures produced subsets that

highly improve the accuracy. However, the number

of attributes in the subsets generated by Information

Gain and Gain Ratio is larger than the number of

attributes in the subset produced by Chi Squared.

We therefore select the subset with seven attributes

produced by Chi Squared. The reduced sets of body

measurements for both males and females are

presented in Table 3.

For the Dutch males, the reduced set of

measurements indicates that the most significant

measurements are the waist circumference, the chest

girth at scye and the vertical trunk circumference.

When tailoring shirts, sweaters or jackets, for the

male population, these measurements should be

considered carefully to produce garments that fit this

population properly. For the Dutch females, the most

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157

important measurements are the bust circumference

and, as in the case of the males, the chest girth at

scye and the vertical trunk circumference. Therefore,

when tailoring clothes for the Dutch females, the

bust circumference requires special attention in

order to design garments that fit the population

better.

By inspecting Table 1 we observe that for the

Italian males, PCA and Gain Ratio produce the best

results. As mentioned previously, PCA presents

additional challenges when applying directly in the

garments design. We then select the subset

generated by Gain Ratio that maximizes the

accuracy. That is, the subset containing fifteen

attributes.

Table 4: Reduced Set of Anthropometric Body

Measurements for the Italian Population.

Males Females

Arm Length

(Shoulder-

Wrist)

Hip Circ Max Height

Arm Length

(Shoulder-Wrist)

Arm Length

(Spine-Wrist)

Knee Height Sitting

Arm Length

(Spine-Wrist)

Buttock Knee

Length

Stature Knee Height Sitting

Chest

Circumference

Thumb Tip Reach Stature

Chest Girth at

Scye

Waist Circumference Thumb Tip Reach

Crotch Height Waist Height

Vertical Trunk

Circumference

Hip Breadth

Sitting

Weight Weight

Hip

Circumference

For the Italian female population, we notice that

the subsets produced by Information gain and Chi

Squared significantly increase the accuracy. The

highest accuracy is achieved using the subset

generated by Information gain that contains seven

attributes. We therefore select this subset of body

measurements for the Italian female population. The

reduced sets containing the most important body

measurements for the Italian population are shown

in Table 4. For the Italian females, the reduced set of

measurements considers the vertical trunk

circumference and the knee height, which are

relevant when, for example, tailoring blouses, skirts

or pants. The vertical trunk circumference is

important when deciding what the length of a jacket

or a blouse should be, in order to produce garments

that are not too short or long for this population.

For the Italian males, the most important

measurements are the chest, waist and hip

circumferences along with the crotch, waist and hip

heights. The measurements, then, address both the

height and girths. This indicates that not only the

height, but also the chest, waist and hip

circumferences should receive special attention

when designing clothes for the Italian males. This,

again, confirms our results that the main

characteristics of the Italian population are related to

height and girths. That is, the Italians are the shortest

and thinnest population.

4.2 Considering Tailoring Practices

In this section, we contrast the results we obtained

during our analysis against the techniques that are in

use in the apparel industry. To this end, we consider

the process used when tailoring a jacket for a female

subject, as found in the literature (Aldrich, 2001,

Schofield and LaBat, 2005, A Suit that Fits, 2009).

When tailoring a jacket, a tailor is instructed to

measure the bust and vertical circumference, the

shoulder width, the arm length from the shoulder to

the wrist, and the length of the centre back to the end

of the jacket.

For illustrative purposes, we consider the results

obtained from the Dutch anthropometric

measurements. Recall, that our study has found that

the most important anthropometric measurements

for the Dutch female population is the chest girth at

scye (i.e. the girth right underneath the arms), the

arm length from the spine to the wrist (recorded

when the arm is bent and the hand rests in the waist),

the bust circumference, the stature, the thumb tip

reach and the vertical trunk circumference. When

comparing our results to current tailoring practices,

we observe the following. The measurements as

obtained by our system are more specific, in the

sense that, for a better fit, the curve of the jacket

sleeves are also taken into account. Similarly, for a

better fit, it follows that also considering the under

arm measurement will give a more comfortable fit

than when simply considering the bust

circumference. Interestingly, the thumb tip reach is

of importance when designing the jacket sleeve

length, to ensure, e.g. in protective clothing design

when handing hazardous materials, that movement is

not restricted when having to reach for an

instrument. Recall that for the Dutch females, the

Medium-sized individuals tend to be very tall with

long arms. This observation is contrary to current

tailoring practices, where the sizes are simply

constructed by increasing the measurements by a

constant value (Aldrich, 2001, Schofield and LaBat,

2005, A Suit that Fits, 2009). Our validation thus

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158

confirms that our approach was able to correctly

identify the subset of measurements that is of

importance, which needs to be incorporated into

current practices to streamline clothing design.

5 CONCLUSIONS

One of the biggest challenges for the apparel

industry is to produce garments that fit the

customers properly and are aesthetically pleasing.

Better characterizations of our populations are thus

needed. Furthermore, the different sizes must

correspond to real body shapes, i.e. one or more

archetypes should represent the individuals

belonging to the same size accurately. In the context

of tailoring, however, the optimal scenario is to

cover the largest number of people with the fewest

number of sizes. Here, it is preferred to have only

one archetype, since each new size increases the

complexity in the manufacturing.

Our approach satisfies the aforementioned

requirements, since we were able to group the

individuals into clusters with a well-defined

Centroid. Our verification, when using the Cleopatra

system, indicates that the cluster membership

corresponds to the reality. Our results show that the

number of body measurements may be significantly

reduced by applying interestingness measure-based

feature selection and feature extraction. Moreover,

these new sets of reduced body measurements

improve the predictive accuracy. These sets contain

the most important body measurements for defining

the body sizes, and may be used in garment design

to identify those body measurements that require

special attention, when tailoring clothes for a

specific population and gender.

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