图 书 馆
频繁的常规项集挖掘Frequent Regular Itemset Mining |
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| 课程网址: | http://videolectures.net/kdd2010_ruggieri_fri/ |
| 主讲教师: | Salvatore Ruggieri |
| 开课单位: | 比萨大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Concise representations of frequent itemsets sacrifice readability and direct interpretability by a data analyst of the concise patterns extracted. In this paper, we introduce an extension of itemsets, called regular, with an immediate semantics and interpretability, and a conciseness comparable to closed itemsets. Regular itemsets allow for specifying that an item may or may not be present; that any subset of an itemset may be present; and that any non-empty subset of an itemset may be present. We devise a procedure, called RegularMine, for mining a set of regular itemsets that is a concise representation of frequent itemsets. The procedure computes a covering, in terms of regular itemsets, of the frequent itemsets in the class of equivalence of a closed one. We report experimental results on several standard dense and sparse datasets that validate the proposed approach. |
| 关 键 词: | 频繁项目集; 直接可解释性; 常规项目集; 稀疏数据集 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-11：cjy |
| 阅读次数: | 58 |