图 书 馆
经济增长：高实用项集挖掘算法UP-Growth: An Efficient Algorithm for High Utility Itemset Mining |
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| 课程网址: | http://videolectures.net/kdd2010_wu_uge/ |
| 主讲教师: | Cheng-Wei Wu |
| 开课单位: | 国立成功大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant approaches have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. In this paper, we propose an efficient algorithm, namely UP-Growth (Utility Pattern Growth), for mining high utility itemsets with a set of techniques for pruning candidate itemsets. The information of high utility itemsets is maintained in a special data structure named UP-Tree (Utility Pattern Tree) such that the candidate itemsets can be generated efficiently with only two scans of the database. The performance of UP-Growth was evaluated in comparison with the state-of-the-art algorithms on different types of datasets. The experimental results show that UP-Growth not only reduces the number of candidates effectively but also outperforms other algorithms substantially in terms of execution time, especially when the database contains lots of long transactions. |
| 关 键 词: | 频繁项集挖掘; 事务数据库; 集高技术 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-12：yumf |
| 阅读次数: | 61 |