An algorithm for fast mining top-rank-k frequent patterns based on Node-list data structure
| Autor: | Darryl N. Davis, Qian Wang, Yongqiang Cheng, Jiadong Ren |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Node (networking) List Rank (computer programming) Preorder Pattern recognition 02 engineering and technology computer.software_genre Data structure Theoretical Computer Science Computational Theory and Mathematics Artificial Intelligence 020204 information systems Transversal (combinatorics) 0202 electrical engineering electronic engineering information engineering Table (database) 020201 artificial intelligence & image processing Pruning (decision trees) Data mining Artificial intelligence business Algorithm computer Software |
| Zdroj: | Intelligent Automation and Soft Computing. 24:399-404 |
| ISSN: | 2326-005X 1079-8587 |
| DOI: | 10.1080/10798587.2017.1340135 |
| Popis: | Frequent pattern mining usually requires much run time and memory usage. In some applications, only the patterns with top frequency rank are needed. Because of the limited pattern numbers, quality of the results is even more important than time and memory consumption. A Frequent Pattern algorithm for mining Top-rank-K patterns, FP_TopK, is proposed. It is based on a Node-list data structure extracted from FTPP-tree. Each node is with one or more triple sets, which contain supports, preorder and post-order transversal orders for candidate pattern generation and top-rank-k frequent pattern mining. FP_TopK uses the minimal support threshold for pruning strategy to guarantee that each pattern in the top-rank-k table is really frequent and this further improves the efficiency. Experiments are conducted to compare FP_TopK with iNTK and BTK on four datasets. The results show that FP_TopK achieves better performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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