影響專案最後成功與否的因子眾多，而且在專案工程生命週期的不同階段，各項因子重要程度是隨時間而改變，因此在專案執行過程中很難精確預測最後的結果，往往只能依賴專案管理人員的經驗來判斷。本研究以CAPP的研究成果為基礎建立專案成功度動態預測資料庫，結合資料探勘技術(Data Mining)，並嘗試以演化式支持向量機推論模式－Evolutionary Support Vector Machine Inference Model（ESIM）為模式核心，有效淬取專家知識及經驗，找出影響專案成功度的顯著因子與專案整體成功度間的映射關係，建立一專案成功度動態預測模式
本研究專案成功度動態預測可分為三個部分：(1)影響專案成功度的顯著因子篩選；(2)專案案例集群分析；(3) 應用ESIM進行成功度預測。第一部分為利用美國CII協會與威斯康辛大學合作一項名為Development of a Predictive Tool for Continuous Assessment of Project Performance 的研究計畫成果-CAPP系統，本研究應用CAPP系統對影響專案成功度的因子利用統計原理進行篩選，找出影響成功度較顯著的因子。第二部分利用k-means法對CAPP資料庫內的46筆歷史案例進行非監督式集群分析，將專案聚類成群內相似度高的群組，以利後續進行ESIM網路訓練。第三部分對分群後的歷史案例應用ESIM進行學習訓練並預測專案最後的成功度，建立專案成功度動態預測模式，並且比較分群前與分群後的預測結果，證明訓練案例藉由資料探勘技術處理後，將可以提高ESIM推論系統預測的準確率。

Various factors in different construction stages can affect a project performance. Due to the impact of the factors changes according to time, the success of project is hard to predict. Problems in prediction of project performance are full of uncertain, vague, and incomplete information. The primary objective of this research is to use the Evolutionary Support Vector Machine Inference Model (ESIM) to develop a dynamic project success prediction model for assisting project managers to predict the project outcomes. The major factors affecting the project success in the construction time frame can also be identified. Thus, to improve the project performance, proper decisions made by project managers are to enforce the management and control of the influencing factors.
This study developed a dynamic project success prediction database based on the research results of the CAPP (Continuous Assessment of Project Performance) system. CAPP system was used to identify the significant factors influencing the project success. Combining the Data Mining technique, the 46 historical construction projects were clustered into groups using the K-means method. Cases with higher similarity were categorized within each cluster to proceed ESIM network training. The training results can be used to predict the success of project. Furthermore, the predictive results before and after clustering were compared to prove that training cases through Data Mining treatment can improve the prediction accuracy of ESIM inference system.

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