循序樣式探勘是一種資料探勘的方法，通常是應用在找出循序資料庫中的頻繁循序樣式。這種傳統的循序資料探勘方法可以被分成二大類，分別是Apriori-like方法和樣式成長方法。而在這二種方法中都使用最小支持度來當做門檻值，並利用最小支持度找出資料庫中的頻繁循序樣式。一般來說，在一個循序樣式之中，每一個事件的機率值都可以提供更多的資訊以供決策者來分析和預測關聯樣式之間的行為。然而，在過去的研究中，並沒有一種技術是可以同時地在樣式挖掘的過程中也發掘出樣式的機率值。因此，為了能夠在循序樣式探勘時也能找出機率的相關資訊，我們提供一個延伸PrefixSpan演算法的方法；該方法為每一個第二階頻繁樣式考慮到機率值。根據這樣的結果，可以建構出一個有方向性的圖形，而這個圖形可以建構成一個所謂的貝氏認知網路。利用貝氏認知網路，可以估計出循序樣式中每一個事件的機率值。 Sequential Pattern Mining is a data mining method that is used to find frequent sequential patterns in a sequential database. The conventional sequence data mining methods can be divided into two categories: Apriori-like methods and Pattern-growth methods. Both of the methods use the minimum support value to be a threshold to discover the sequential patterns. The probability of each event in a sequential pattern can provide more information for decision maker to analyze and predict the behavior of correlated pattern. However, in the previous studies there is no technique developed to simultaneously discover the probability in the pattern mining process. Thus, to provide such information, we will extend the PrefixSpan method with considering the probability for each level 2 pattern. According to the results, a directed graph will be constructed to build a Bayesian Belief Network (BBN). Using the BBN, the probability of each event in a sequential pattern can be evaluated.
