The minimum description length principle for pattern mining: a survey. (English) Zbl 1509.68240
Summary: 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.
MSC:
| 68T09 | Computational aspects of data analysis and big data |
| 68P30 | Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science) |
| 68T10 | Pattern recognition, speech recognition |
| 68-02 | Research exposition (monographs, survey articles) pertaining to computer science |
Keywords:
data mining; pattern mining; frequent itemset mining; minimum description-length principle; information theoryReferences:
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