資料串流探勘是一個新興的研究領域，而關聯規則演算法是資料探勘 (Data Mining)中一項相當重要且實用的技術。關聯式法則最主要的目的就是在龐大的資料中，把一些資料項目的相關性找出來，主要的方法是搜尋資料庫找出所有的高頻項目組；並且利用高頻項目組挖掘出所有的關聯式法則。因為大量的資料及大量候選資料項目組的產生，所以找出所有的高頻項目組是一件必須耗費大量計算成本的工作。因此如何有效的減少在計算時的資料量，減少候選資料項目組的產生，並且減少資料庫的讀取次數等，都能夠使關聯式法則挖掘的演算法更有效率。 我們研究的主要內容利用建構規則編碼(Run-Length Encoding)方式在動態資料庫中有效的減少關聯式法則演算法在計算時的資料量，主要的貢獻是提供一種新的資料前處理的方法，其利用將交易資料庫編碼成少量的資料，然後直接對主記憶體中的編碼資料進行資料探勘，並且在快速資料異動時能夠有效更新編碼資料，加速演算法的執行速度，提升處理效能 。 It is a new developing research field that the materials bunch flows and prospects, and the RLEated rule performs algorithms and is prospected by the materials (Data Mining) A quite important and practical technology in China. The RLEated type rule main purpose is to find out the dependence of some materials projects in the huge materials. The main method is to search the database and find out all high-frequency project teams; And utilize the high-frequency project team to excavate out all RLEated type rules.Because of the production of a large number of materials and a large number of candidate materials project teams, it is that one must consume a large number of work of calculating the cost to find out all high-frequency project teams. So, what effective materials amount while calculating of reduction, reduce the production of the project team of candidate materials, and reduce reading number of times,etc. of the database, the algorithm of performing that can make the RLEated type rule excavate is more efficient. Main content that we study utilizes and builds and constructs the regular code (Run-Length Encoding) The valid RLEated type rule of reduction in the dynamic database of the way performs the materials amount of algorithms while calculating, main contribution is the method of offering a kind of new materials to deal with, it utilized and traded the database and encoded a small amount of materials, then prospect the materials to the code materials mainly in the storing device directly, and can upgrade and encode the materials effectively in the unusual fluctuation of fast materials, perform the speed of execution of the algorithm with higher speed, improve and deal with efficiency.
