Name
Abstract | ||
|---|---|---|
Pattern Mining (PM) has a prominent place in Data Science and finds its application in a wide range of domains. To avoid the exponential explosion of patterns different methods have been proposed. They are based on assumptions on interestingness and usually return very different pattern sets. In this paper we propose to use a compression-based objective as a well-justified and robust interestingness measure. We define the description lengths for datasets and use the Minimum Description Length principle (MDL) to find patterns that ensure the best compression. Our experiments show that the application of MDL to numerical data provides a small and characteristic subsets of patterns describing data in a compact way. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/DCC.2019.00019 | 2019 Data Compression Conference (DCC) |
Keywords | Field | DocType |
Pattern Mining,Minimum Description Length,Data Mining | Compression (physics),Computer vision,Engineering drawing,Computer science,Artificial intelligence | Conference |
ISSN | ISBN | Citations |
1068-0314 | 978-1-7281-0658-8 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tatiana P. Makhalova | 1 | 3 | 5.10 |
| Sergei O. Kuznetsov | 2 | 1630 | 121.46 |
| Amedeo Napoli | 3 | 1180 | 135.52 |