資料挖礦應用的領域十分廣泛，但在運用人體計測資料來製作成衣尺碼系統上，卻未見相關的研究。本研究針對台灣地區人體計測資料庫，以女性上衣和軍人下褲建構一套制定成衣尺碼系統的資料挖礦循環，利用決策樹技術結合領域專家的判斷，從大量的數據資料中進行探索和分析，首先，先定義問題；第二，將資料作準備與分析，包括資料的處理與轉換並進行因素分析，然後選擇重要的尺碼變數；第三，以決策樹進行資料挖礦，挖掘出有意義的樣型或規則，並進行體型的分類；最後建立四種體型的成衣尺碼系統並進行結果討論與應用。本研究發現，利用資料挖礦的決策樹技術所建立的尺碼系統具有以下優點：（1）涵蓋率高而且尺碼的組數少；（2）可提供廠商有利於成衣生產的參考。新建立的尺碼系統除可提供成衣業者詳細的體型尺寸、設計開發、版型放縮和市場分析等資訊外，過去由於原料與產品的不協調所造成存貨成本的浪費也可以降到最低，使生產管理的規劃更實際可行。

While data mining has been widely used in many fields, little research has been done on its applications to sizing systems for the manufacturing of garment. The goal of this study was to establish systems for using a decision tree technique to determine the upper-clothes sizes of women and the pants sizes of army soldiers. This study first defined the objective and constructed a large anthropometric database. Second, this study prepared and analyzed data, performed factor analyses, and then extracted important sizing variables. Third, this study used decision tree technique to conduct data mining in an effort to identify significant patterns in the body types of subjects and classify them. This study established the sizing systems and then discussed the results and the applications. The use of decision tree-based data mining to establish sizing systems are advantageous because they can (1) allow for a wider coverage of body types with a fewer number of sizes, and (2) provide manufacturers with reference points to facilitate production. As a result, the newly developed sizing systems can provide garment manufacturers with size specification, design development, pattern grading, and market analysis. Moreover, production plans can be made more realistic and inventory costs due to mismatches can be minimized.

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