本研究的目的在於從民航飛安檢查員的日常飛安查核結果中，發掘潛在關聯規則。查核資料以月份為單位，將安全相關狀態彙總整理計算出查核不滿意率。在準備分析資料中，先清除多餘不需要的資料，並應用修正後的MVC(Missing Values Completion)法來處理屬性資料的遺漏問題。而修正後的MVC法使用SOM(Self-Organizing Map)群聚技術來將資料進行分群。在同ㄧ群的資料紀錄中擁有著相似的資料型態。根據假設，以同ㄧ群集中計算出的beta平均值來填補遺漏項目。 我們使用Agrawal et al. (1993)提出之Apriori 關聯規則演算法來分析資料。由於Apriori演算法無法處理數值資料，因此在使用該演算法之前，將績效指標根據統計處理控制技術轉換成為正常與非正常之邏輯形態。除此之外，亦使用傳統的Pearson Correlation Analysis來了解飛安事件與飛安檢查結果之關聯。在本研究中，將考慮「時間遞移」的問題，並從中找出之間的關聯性。 The purpose of this research is to discover any potential association rules for aviation safety inspection results which are performed daily by CAA aviation safety inspector. The inspection data will be aggregated to identify the unfavorable rate for each safety related performance in one month period. To prepare the analyzed data, we clean the redundant data and apply a modified MVC（Missing Values Completion）method to deal with attribute value missing. The modified MVC method uses the SOM (self-organization map) clustering technology to classify data records into clusters. The data records in the same cluster have similar data pattern. According to the assumption, the beta mean value in the same cluster is calculated to fill into the missing attribute. We applied the Apriori association rule algorithm described by Agrawal et al. (1993) to the analyzed data. Since the Apriori algorithm does not process numerical data, we transform the performance attributes to the set of discrete categories, normal and abnormal, by a statistic process control technique before application of the algorithm. Besides, the traditional Pearson correlation analysis has also been conducted to figure out the relationship between aviation events and safety inspection results. In our research, time lag has been considered as an important issue to discover such a relationship.
