The minimum description length principle for pattern mining: a survey

被引：0
|
作者：
Esther Galbrun
机构：
[1] University of Eastern Finland,School of Computing
来源：
Data Mining and Knowledge Discovery | 2022年 / 36卷
关键词：
Data mining; Pattern mining; Frequent itemset mining; Minimum description length principle; Information theory;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration, the selection of patterns constitutes a major challenge. The Minimum Description Length (MDL) principle, a model selection method grounded in information theory, has been applied to pattern mining with the aim to obtain compact high-quality sets of patterns. After giving an outline of relevant concepts from information theory and coding, we review MDL-based methods for mining different kinds of patterns from various types of data. Finally, we open a discussion on some issues regarding these methods.
引用
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页码：1679 / 1727
页数：48
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