Department of Computer Science and Information Engineering,National Dong Hwa University, Taiwan, R.O.C.

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Abstract—Mining of frequent patterns in databases has been studied for several years. However, real-world databases contain noise and frequent pattern mining which extracts patterns that are absolutely matched is not enough. Therefore, a research field called fault-tolerant frequent pattern (FT-pattern) mining is proposed to deal with this problem. In this paper, we consider the problem of mining proportional FT-patterns. That is, the number of faults tolerable in a pattern is proportional to the length of the pattern. To reduce the disk I/O times, a depth-first mining approach is proposed to mine proportional FT-patterns efficiently in large database. Moreover, a set of experiments is performed to show the advantage of the approach. Experimental results indicate that the proposed algorithm outperforms the other existing approach when the database size is large.

Index Terms—Data mining, association patterns, fault-tolerant patterns.

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Cite: Chih-Chieh Tseng, Guanling Lee, "A Depth-First Search Approach for Mining Proportional Fault-Tolerant Frequent Patterns Efficiently in Large Database," Journal of Computers vol. 10, no. 6, pp. 388-395, 2015.