本篇論文中我們提出一個在RFID的環境架構下觀察使用者兩段時期行為變化性的方法，一開始先以欲觀察的檢查點將使用者連續的移動紀錄分成兩段，然後利用資料探勘技術針對兩段時期的記錄分別進行多種常見的行為模式探勘以建立出使用者的行為模組，最後以計算模組之間差異性的方式來分析行為的變化性並且以數值量化出變化的程度。在本篇的研究方法中，我們共使用了資料探勘領域中最為熱門的關聯樣式與序列型樣，以及位移頻率與速度三種常見的行為模式，並且提出各自的差異性計算方法，對於兩段時期的變化每種行為模式都會算出一個代表的數值，最後再以權重分配的方式整合就可得到模組之間的整體差異性。在過去的研究中，基於多種的研究目的，已有許多學者提出數種針對關聯樣式以及序列型樣的差異性計算方法，本篇中我們是以較為常見的方法加以修改，再加入新的行為模式的差異性計算，就目前所知，本篇是最早提出在RFID環境架構下，藉由整合多種行為模式探勘來計算以及分析使用者變化性的研究，所以在本篇論文的後半段，我們也會利用貼近真實情況的模擬環境及一系列相關的實驗，來證明這些計算方式是合理的。

In this paper, we propose a novel hybrid approach for analysis of patient behaviors by mining the streamed movement log obtained in RFID environments. The proposed approach can discover the regular behavior models of patients using data mining techniques. Moreover, the variation between two behavior models under different periods can also be obtained. We propose three kinds of novel variation evaluation strategies, namely association pattern, sequential pattern, and location change frequency/velocity that utilize the evaluation result to objectively evaluate the variation between two periods on patients. To our best knowledge, this is the first work on evaluating the behavior variation of patients by mining behavior patterns in RFID-based patient monitoring environments. Through a series of experimental evaluations, the proposed hybrid method is shown to be promisingly effective under different system conditions.

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