图 书 馆
基于零不变测度的顶相关模式的有效挖掘Efficient Mining of Top Correlated Patterns Based on Null-Invariant Measures |
|
| 课程网址: | http://videolectures.net/ecmlpkdd2011_han_measures/ |
| 主讲教师: | Jiawei Han |
| 开课单位: | 伊利诺伊大学 |
| 开课时间: | 2011-11-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Mining strong correlations from transactional databases often leads to more meaningful results than mining association rules. In such mining, null (transaction)-invariance is an important property of the correlation measures. Unfortunately, some useful null-invariant measures such as Kulczynski and Cosine, which can discover correlations even for the very unbalanced cases, lack the (anti)-monotonicity property. Thus, they could only be applied to frequent itemsets as the post-evaluation step. For large datasets and for low supports, this approach is computationally prohibitive. This paper presents new properties for all known null-invariant measures. Based on these properties, we develop efficient pruning techniques and design the Apriori-like algorithm NICOMINER for mining strongly correlated patterns directly. We develop both the threshold-bounded and the top-k variations of the algorithm, where top-k is used when the optimal correlation threshold is not known in advance and to give user control over the output size. We test NICOMINER on real-life datasets from different application domains, using Cosine as an example of the null-invariant correlation measure. We show that NICOMINER outperforms support-based approach more than an order of magnitude, and that it is very useful for discovering top correlations in itemsets with low support. |
| 关 键 词: | 计算机科学; 数据挖掘; 频繁模式 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-13：yumf |
| 阅读次数: | 51 |