臺灣博碩士論文加值系統

尺寸制定系統(Sizing system)對於服裝設計及生產是一項重要的課題，服裝尺寸的準確性關係到顧客的滿意度和生產製造。過去許多研究提出了關於尺寸制定系統的建立，而本研究旨在透過新穎的資料探勘方法，以人體計測資料(anthropometry data)為基礎，發展一套新的尺寸制定系統。
本研究採用以啟發式演算法為基礎的分群方法，為成衣工業確立新的標準尺寸制定系統，透過量測912位年齡介於18至25歲的受測對象(598位男性、304位女性)之人體計測資料，本研究提出一套適用於印尼成年人的尺寸制定系統，其包含七個變量：臀寬(hip width)、臂長(arm length)、腰寬(waist width)、胸寬(bust width)、背長(back-waist length)、(back-rest width)以及身材(stature)。
本研究提出兩階段的方法，第一階段採用主成分分析(PCA, principal component analysis)進行特徵擷取，接者，採用數個以萬用啟發式演算法為基礎的方法以尋得符合群體之最佳尺寸制定系統。經由數據分析的結果顯示，本實驗使用之數據可行的大小為5群，此外，基於所述集合的損失(aggregate loss)，本研究之模型具有良好的準確性，同時可作為成衣工業提供服裝尺寸的建議，為客戶提供正確、合適的尺寸。

Sizing system is essential for apparel design and production. Accurate size of apparel is related to customer satisfaction and manufacturing. Several researches had been proposed to create sizing system. This study aims to develop a new sizing system for anthropometry data using novel data mining approach. This study employs metaheuristic-based clustering techniques to determine a new standard sizing system for apparel industry. Through measuring anthropometry of 912 objects (598 males and 304 females) aged between 18 and 25, this study proposes a sizing system for Indonesian adult with seven variables, hip width, arm length, waist width, bust width, back-waist length, back-rest width, and stature. There are two stages for the proposed method. The first stage employs principal component analysis (PCA) for feature extraction. Then, several metaheuristic-based techniques will be employed to find the best sizing system which fit to the population and hybridized with fuzzy c-means. The computational result indicated that five groups of size are feasible for the current data. In addition, based on the aggregate loss the proposed model has a good accuracy and the result can be used as a size recommendation to specify the right size for the customers.

摘要 i
ABSTRACT ii
ACKNOWLEDGEMENTS iii
CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES vii
CHAPTER 1 INTRODUCTION 1
1.1 Background &; Motivation 1
1.2 Problem Definition 3
1.3 Research Objectives 3
1.4 Research Scope and Assumptions 4
1.5 Research framework 4
CHAPTER 2 LITERATURE SURVEY 6
2.1 Standard Sizing System 6
2.2 Principal Component Analysis 8
2.3 Cluster analysis 9
2.3.1 Measures of dissimilarity 10
2.3.2 K-means Algorithm 11
2.3.3 Fuzzy c-means algorithm 12
2.4 Metaheuristic-based Clustering Analysis 14
2.4.1 Particle Swarm Optimization based K-means Clustering 14
2.4.2 Genetic Algorithm based K-means Clustering 15
2.4.3 Artificial Bee Colony based K-means Clustering 16
CHAPTER 3 METHODS 20
3.1 Standard Sizing System Framework 20
3.2 Selection of anthropometric dimensions 21
3.3 Data Preprocessing 22
3.4 Principal Component Analysis 23
3.5 Fitness Function 24
3.6 Metaheuristic Based Clustering 25
3.6.1 Particle Swarm Optimization (PSO) based Clustering 25
3.6.2 Artificial Bee Colony (ABC) based Clustering 27
3.6.3 Genetic Algorithm (GA) based Clustering 29
CHAPTER 4 COMPUTATION AND RESULT 31
4.1 Data Sets 31
4.2 Data Reduction 33
4.3 Parameter Setting 34
4.3.1 Parameters of Genetic Algorithm 34
4.3.2 Parameters of Particle Swarm Optimization 35
4.3.3 Parameters of Artificial Bee Colony 36
4.3.4 Cluster number 37
4.4 Experimental Result and Analysis 37
4.4.1 Convergence Analysis 38
4.5 Standard Sizing Chart 39
4.6 Validation of Proposed Standard Sizing 43
4.7 Application of new industrial standards 45
CHAPTER 5 CONCLUSIONS AND FUTURE WORK 46
5.1 Conclusions 46
5.2 Contributions 46
5.3 Future Study 47
REFERENCES 48
APPENDIX I BODY DIMENSION 52
A.1.1 Description of Body Dimension 52
APPENDIX II RUNNING RESULT 54
A.2.1 Fitness Value 54
APPENDIX III PRINCIPAL COMPONENT ANALYSIS RESULT 56
A.3.1 Coefficient Matrix 56
A.3.2 Eigenvalue of Data Set 56
APPENDIX IV AGGREGATE LOSS OUTPUT 58
A.4.1 Aggregate Loss and Number of Individual 58

Abraham, A., Das, S., Roy, S. 2008. Swarm intelligence algorithms for data ,clustering, Soft Computing for Knowledge Discovery and Data Mining, Part IV, 279-313.
Bagherzadeh, R., Latifi, M., and Faramarzi, A.R. 2010. Employing a Three-Stage Data Mining Procedure to Develop Sizing System, World Applied Sciences Journal, 8(8), 923-929.
Clarke, B., Fokoue, E., and Zhang, H.H. Principles and Theory for Data Mining and Machine Learning, New York: Springer.
Chung, M.J., Lin, H.F., and Wang, M.J.J. 2007. The development of sizing system for Taiwanese elementary-and high-school students, International Journal of Industrial Ergonomics, 37(8), 707-716.
Ciflikli, C. and Ozyirmidokuz, E.K.. 2010. Implementing a data mining solution for enhancing carpet manufacturing productivity, Knowledge-Based Systems, 23(8), 783-788.
Fogel, L.J., Owens A.J., and Walsh, M.J. 1966. Artificial Intelligence Through Simulated Evoluton. New York: John Wiley &; Sons.
Gupta, D., and Gangadhar, B.R. 2004. A statistical model for developing body size charts for garments, International Journal of Clothing Science and Technology, 16(5), 458-469.
Gungor, Z., and Unler, A. 2007. K-harmonic means data clustering with simulated annealing heuristic, Applied Mathematics and Computation, 184(2), 199-209.
Han, J. and Kamber, M. 2007. Data Mining: Concepts and Techniques. San Fransisco, California: Morgan Kaufman.
Hashmi, A., Gupta, D., Upadhyay, Y., and Goel, S. 2013. Swarm intelligence based approach for data clustering, International Journal of Innovative Research &; Studies, 2(6), 572-589.
Holland, J.H. 1975. Adaptation in Natural and Artificial Systems, Oxford, England: University of Michigan Press.
Hsu, C.H. 2009a. Data mining to improve industrial standards and enhance production and marketing: An empirical study in apparel industry, Expert Systems with Applications, 36(1), 4185-4191.
Hsu, C.H. 2009b. Developing accurate industrial standards to facilitate production in apparel manufacturing based on anthropometric data, Human Factors and Ergonomics in Manufacturing &; Services Industries, 19(3), 199-211.
Hsu, C.H. and Wang, M.J.J. 2005. Using decision tree-based data mining to establish a sizing system for the manufacture of garments, International Journal of Advance Manufacturing Technology, 26(5-6), 669-674.
Hsu, C.H., Lee, T.Y., and Kuo, H.M. 2007. Mining the body features to develop sizing systems to improve business logistics and marketing using fuzzy clustering data mining, WSEAS Transactions on Computers, 8(7), 1215-1224.
Ibanez, M.V., Vinue, G., Alemany, S., Simo, A., Epifanio, I., Domingo, J., and Ayala, G. 2012. Apparel sizing using trimmed PAM and OWA operators, Expert System with Applications, 39(12), 10512-10520.
International Standards for Anthropometric Assessment. 2001. International Soziety for the Advancement of Kinanthropometry, Australia.
ISO/TR 10652, 1991. Standard sizing system for clothes. International Organization for Standardization, Geneva.
Jollife, I.T. 2004. Principal Component Analysis second ed., New York: Springer.
Karaboga, D. 2005. An idea based on honey bee swarm for numerical optimization. Technical report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department.
Karaboga, D. and Ozturk, C. 2011. A novel clustering approach: Artificial Bee Colony (ABC) algorithm, Applied Soft Computing, 11(1), 652-657.
Kennedy, J., and Eberhart, R. Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, November 27-December 1, 1995.
Kononenko, I. and Kukar, M. 2007. Data Mining: Introduction to Principles and Algorithms, Chichester, UK: Horwood Publishing.
Knorr, E.M., Ng, R.T., and Tucakov, V. 2000. Distance-based outliers: algorithms and applications, The VLDB Journal, 8(3-4), 237-253.
Kwon, O., Jung, K., You, H., and Kim, H.E. 2009. Determining of key dimensions for a glove sizing-system by analyszing the relationships between hand dimensions, Applied Ergonomics, 40(4), 762-766.
Luximon, A., Zhang, Y., Luximon, Y., and Xiao M. 2012. Sizing and grading for wearable products, Computer-Aided Design, 44(1), 77-84.
MacQueen, J. Some methods for classification and analysis of multivariate observations, Proceeding of 5th Berkeley Symposium on Mathematical Statistics and Probability, California, USA, June 21-July 18, 1967.
Maulik, U. and Bandyopadhyay, S. 2000. Genetic algorithm-based clustering technique, Pattern Recognition, 33(9), 1455-1465.
McCulloch, C.E., Paal, B., and Ashdown, S.P., An optimisation approach to apparel sizing, Journal of the Operational Research Society, 49(5), 492-499.
Omran, M.G.H., Engelbrecht, A.P., Salman, A. 2006. Particle Swarm Optimization for Pattern Recognition and Image Processing, Swarm Intelligence in Data Mining, 34, 125-151.
Pena, I., Viktor, H.L., and Paquet, E., Explorative Data Mining for the Sizing of Population Groups, Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, Madeira, Portugal, October 6-8, 2009.
Pyle, D. 1999. Data Preparation for Data Mining. California: Morgan Kaufman.
Shahrabi, J. and Hadavandi, E. 2013. Developing a hybrid intelligent model for constructing a size recommendation expert system in textile industries, International Journal of Clothing Science and Technology, 25(5), 338-349.
Tan, P.N., Steinbach, M., and Kumar, V. 2006. Introduction to Data Mining, Boston, MA: Addison-Wesley.
Tryfos, P. 1986. An integer programming approach to the apparel sizing problem, Journal of the Operational Research Society, 37(10), 1001-1006.
Viktor, H.L., Paquet , E., and Guo, H., Measuring to fit: virtual tailoring through cluster analysis and classification, Proceedings of 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Berlin, Germany, September 18-22, 2006.
Xu, R. and Wunsch, D. 2005. Survey of clustering algorithms, Neural Networks, IEEE Transaction on, 16(3), 645-678.
Zadeh, L.A. 1965. Fuzzy sets, Information and Control, 8(3), 338-353.
Zhao, L., Tsujimura, Y., and Gen, M. Genetic algorithm for fuzzy clustering, Proceeding of Evolutionary Computation, 1996.
Zhang, C., Ouyang, D., and Ning, J. 2010. An artificial bee colony approach for clustering, Expert Systems with Applications, 37(7), 4761-4767.
Zheng, R., Yu, W., and Fan, J. 2007. Development of a new Chinese bra sizing system based on breast anthropometric measurements, International Journal of Industrial Ergonomics, 37(8), 697-705.