傳統(tǒng)序列模式挖掘算法往往忽略了序列模式本身的時(shí)間特性，所考查的序列項(xiàng)都是單
一事件，無屬性約束。提出了一種挖掘多屬性約束事件序列關(guān)聯(lián)規(guī)則的方法。此方法基于傳統(tǒng)的Apriori 和AprioriAll 算法，考慮了應(yīng)用環(huán)境下事件序列模式中事件之間的過渡時(shí)間，采用分層式挖掘思想，先挖掘頻繁序列模式，然后從頻繁事件序列中挖掘多屬性約束項(xiàng)的關(guān)聯(lián)規(guī)則。實(shí)例分析為挖掘帶時(shí)間限多屬性約束的序列模式提供了實(shí)施思路。
關(guān)鍵字：序列模式，屬性約束，關(guān)聯(lián)規(guī)則
Abstract: The time trait is often ignored in the course of mining traditional sequential pattern, in which the sequential item is also without attribute constraint. An idea is given to mine event sequential pattern with multi-attribute constraint in this paper. Based on the algorithm of Apriori and Apriori, the transition time is taken into account between events. According to the layer idea, the key task is to mine the frequent sequential pattern first, then to find out the association rules in the attribute constraint item. In the end it provides a way to mine sequential pattern with multi-attribute constraint by example analysis.
Key Words: sequential pattern; attribute constraint; association rule