隨著資訊科技進步與電腦的普及，企業逐漸開始建構屬於自己的資料庫，資料亦被大量的儲存與紀錄下來。企業擁有更多的資料來獲取更準確的答案，但也由於資料的大量與複雜度，亦增加使用者萃取資訊的困難度，降低其資料的價值，而資料挖礦可從大量資料中以自動或是半自動的方式來探索（explore）和分析資料，以發掘出潛在有用的資訊。在現代企業製商整合與電子化之經營環境下，決策者可以應用資料挖礦技術萃取出有價值的資訊或原本隱藏不知的特殊樣型，以處理大量資料混雜時的決策問題。本研究目的在於整合決策樹與關聯規則之方法，架構一適用於一般性事故排除問題的資料挖礦架構，並以台灣電力公司配電事故表與某半導體廠實際資料與進行實證研究，以驗證此架構之效度。

As the progress of IT and computer science, the companies begin to build their own database to store and record the data and to obtain more exact benefits. On the other hand, owing to the complexity, it is more difficult to obtain the information they want that lower the value of the data. Data mining can explore useful information and analyze it from a great deal of data in automative and semi-automative ways. In morden business environment of e-Manufacturing and e-Commerce, the decision maker can make decision based on valuable information and hidden pattern from data mining methods. This research aims to integrate the methodology of Decision Tree and Association Rule and build a architecture of data mining which is suitable for common troubleshooting problem-solving event. Finally, we have the real case studies on distribution feeder faults of Taiwan Power Company and actual engineering data of one semiconducter foundry. The result shows practical viability of data mining approach for troubleshooting.

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