隨著電腦軟體的發展，企業利用流程資料庫，儲存流程事件檔已是一個非常普遍的方式，而由於商業流程的越趨複雜，流程資料庫所儲存的事件檔也就相對的非常冗長，而如此探勘出的流程架構圖，不是概括性的描述整個商業流程活動，便是過於複雜讓使用者無法容易了解，也無法輕易的擷取出，使用者所需的正確資訊。因此，如何對一個資料量非常龐大的流程資料庫，進行分群的方式，讓使用者能清楚的了解不同群組間的差異性，以及透過對分群的流程探勘，有效的取得使用者所需的正確資訊，讓使用者在看到流程圖時，就能透過圖的表達方式，快速了解商業流程的詳細架構，是一個非常重要的問題。
因此，本研究將提出一個方法，針對流程資料庫中的流程事件檔進行分群，以此分析出造成流程差異性的原因，並透過流程探勘的方式，找出潛藏的流程資訊。
本研究提出以區塊式的方式，對流程事件檔進行切割，將分群的區塊縮小，藉此簡化在進行分群時的計算複雜度。並運用給予不同的Transition權重，增加流程相關性的敏感度，以及不同分群群組間的差異性，最後在針對每個分群結果的流程事件檔，進行流程探勘，建構個別的流程模型，讓使用者了解不同分群間的差異性，並擷取所需的正確資訊。

Recently, workflow automation has been widely applied in industry. Log files stored the activities sequence for each case can be used to construct the process model in terms of the developed process mining algorithm. However, due to the complexity of the process, the mined model might be very complicated and hence difficult to view and to analysis. Cluster the stored cases and to mine each group of cases can simplified this issue.
　Currently, the developed workflow clustering algorithm tends to compute the sequential relationship of the activities based on the whole record of the case. The computational efficiency will decrease a lot for a case with long sequence of activities.
　In this research, an approach based on blocking the log into several group and clustering the activity data in each group will be proposed. This approach ignores the sections of the records having the common sequential relationship and addresses on the portions where cases have diverse string of activities. That is, based on the mined model, the sequence of activities in the log will be classified as several blocks. Then the activities in each block will be clustered and the inter-block activities relationship also will be analyzed. By applying this approach, the computation efficiency will be increased for the log with long string of activities but contained common sequential relationship. In addition, the attributes for each group of cases can be analyzed by identifying the features of individual block of activities.

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