Performance and characteristic analysis of maximal frequent pattern mining methods using additional factors
| Autor: | Unil Yun, Gangin Lee |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Computer science Computational intelligence 02 engineering and technology computer.software_genre Theoretical Computer Science 020901 industrial engineering & automation Knowledge extraction Order (business) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Geometry and Topology Data mining K-optimal pattern discovery computer Software |
| Zdroj: | Soft Computing. 22:4267-4273 |
| ISSN: | 1433-7479 1432-7643 |
| DOI: | 10.1007/s00500-017-2820-3 |
| Popis: | Various data mining methods have been proposed to handle large-scale data and discover interesting knowledge hidden in the data. Maximal frequent pattern mining is one of the data mining techniques suggested to solve the fatal problem of traditional frequent pattern mining approach. While traditional approach may extract an enormous number of pattern results according to threshold settings, maximal frequent pattern mining approach mines a smaller number of representative patterns, which allow users to analyze given data more efficiently. In this paper, we describe various recent maximal frequent pattern mining methods using additional factors and conduct performance evaluation in order to analyze their detailed characteristics. |
| Databáze: | OpenAIRE |
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