萬用啟發式演算法為基礎之模糊 c-means演算法於服裝尺碼系統之研究

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研究生：	沈孝明 Agus - Pahala Simbolon
論文名稱：	萬用啟發式演算法為基礎之模糊 c-means演算法於服裝尺碼系統之研究 Metaheuristic-based fuzzy c-means Algorithm for Apparel Sizing System
指導教授：	郭人介 Ren-Jieh Kuo
口試委員:	歐陽超 Chao Ou-Yang 蔡介元 Chieh-Yuan Tsai
學位類別：	碩士 Master
系所名稱：	管理學院 - 工業管理系 Department of Industrial Management
論文出版年：	2014
畢業學年度：	102
語文別：	英文
論文頁數：	69
中文關鍵詞：	尺寸制定系統、人體計測資料、分群分析、萬用啟發式演算、主成分分析
外文關鍵詞：	Sizing system, Anthropometry, Cluster analysis, Metaheuristic optimization, Principal component analysis
相關次數：	點閱：393 下載：9
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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.

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