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Abstract Mining Fault-tolerant Frequent Patterns Efficiently with Powerful Pruning
| Content Provider | CiteSeerX |
|---|---|
| Author | Zeng, Jhih-Jie Lee, Guanling Lee, Chung-Chi |
| Abstract | The mining of frequent patterns in databases has been studied for several years. However, the real-world data tends to be dirty and frequent pattern mining which extracts patterns that are absolutely matched is not enough. An approach, called fault-tolerant frequent pattern (FT-pattern) mining, is more suitable for extracting interesting information from real-world data that may be polluted by noise. In our approach, both of the problems of mining proportional and fixed FT-patterns are considered. In proportional FT-pattern mining, the number of faults tolerable in a pattern is proportional to the length of the pattern. And the number of faults tolerable in different length of patterns is fixed in fixed FT-pattern mining. A new graph structure, FT-association graph, is proposed to help us filtering out impossible candidates with high efficiency. The experimental results show that the proposed algorithms of our approach are highly efficient for mining both proportional and fixed FT-patterns. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Abstract Mining Fault-tolerant Frequent Pattern Fixed Ft-patterns Fixed Ft-pattern Mining Real-world Data Tends Several Year Ft-association Graph Fault-tolerant Frequent Pattern New Graph Structure Different Length Interesting Information Real-world Data Impossible Candidate Proportional Ft-pattern Mining Frequent Pattern Mining Frequent Pattern High Efficiency |
| Content Type | Text |
