事件序列上的频繁情节挖掘算法  

Algorithms for Mining Frequent Episodes on the Event Sequences

作　　者：丁勇[1] 王云[1] 李丛[1] 

机构地区：[1]南京理工大学泰州科技学院,泰州225300

出　　处：《计算机系统应用》2014年第12期202-205,共4页Computer Systems & Applications

摘　　要：事件序列上的频繁情节挖掘是时序数据挖掘领域的热点之一,基于非重叠发生的支持度定义,提出一个频繁情节挖掘算法NONEPI++,该算法首先通过情节串接产生候选情节,然后通过预剪枝和计算情节发生的时间戳来产生频繁情节.算法只需扫描事件序列一次,大大提高了情节挖掘的效率.实验证明,NONEPI++算法能有效地挖掘频繁情节.Mining frequent episodes on the event sequences is one of the hot areas of data mining. In this paper, support based on non-overlapped occurrence is definited. We propose an algorithm called NONEPI＋＋ for mining frequent episodes, which firstly generate candidate episodes by join episodes, then generate frequent episodes by pre-pruning and timestamp calculating. The algorithm can improve the efficiency of mining episodes. Experiments show that NONEPI＋＋algorithm can effectively mine frequent episodes.

关 键 词：事件序列 频繁情节 非重叠发生 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]