| 事件序列上的频繁情节挖掘算法 |
| |
| 引用本文： | 丁勇,王云,李丛.事件序列上的频繁情节挖掘算法[J].计算机系统应用,2014,23(12):202-205. |
| |
| 作者姓名： | 丁勇 王云 李丛 |
| |
| 作者单位： | 南京理工大学泰州科技学院,泰州,225300 |
| |
| 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.
|
| 关 键 词： | 事件序列 频繁情节 非重叠发生 |
| 收稿时间： | 4/1/2014 12:00:00 AM |
| 修稿时间： | 5/4/2014 12:00:00 AM |
Algorithms for Mining Frequent Episodes on the Event Sequences |
| |
| DING Yong,WANG Yun and LI Cong.Algorithms for Mining Frequent Episodes on the Event Sequences[J].Computer Systems& Applications,2014,23(12):202-205. |
| |
| Authors: | DING Yong WANG Yun and LI Cong |
| |
| Affiliation: | Taizhou College of Science and Technology, NJUST, Taizhou 225300, China;Taizhou College of Science and Technology, NJUST, Taizhou 225300, China;Taizhou College of Science and Technology, NJUST, Taizhou 225300, China |
| |
| Abstract: | 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. |
| |
| Keywords: | event sequence frequent episode non-overlapped occurrence |
| 本文献已被 维普 万方数据 等数据库收录！ |
| 点击此处可从《计算机系统应用》浏览原始摘要信息 |
|
点击此处可从《计算机系统应用》下载全文 |
|
