当前位置: X-MOL 学术Data Min. Knowl. Discov. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
The minimum description length principle for pattern mining: a survey
Data Mining and Knowledge Discovery ( IF 2.8 ) Pub Date : 2022-07-04 , DOI: 10.1007/s10618-022-00846-z
Esther Galbrun

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.



中文翻译:

模式挖掘的最小描述长度原则:调查

挖掘模式是数据分析中的一项核心任务,除了有效枚举的问题之外,模式的选择也是一项重大挑战。最小描述长度(MDL)原则是一种基于信息论的模型选择方法,已应用于模式挖掘,旨在获得紧凑的高质量模式集。在概述了信息论和编码的相关概念之后,我们回顾了基于 MDL 的方法,用于从各种类型的数据中挖掘不同类型的模式。最后,我们就这些方法的一些问题展开讨论。

更新日期:2022-07-05
down
wechat
bug