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

页码：1679 / 1727

页数：48

共 50 条

[1] The minimum description length principle for pattern mining: a survey
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[J]. DATA MINING AND KNOWLEDGE DISCOVERY, 2022, 36 (05) : 1679 - 1727
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