Abstract
Mining of frequent patterns in database has been studied for several years. However, real-world data tends to be dirty and frequent pattern mining which extracts patterns that are absolutely matched is not enough. An approach, called frequent fault-tolerant pattern (FT-pattern) mining, is more suitable for extracting interesting information from real-world data that may be polluted by noise. Previous research on frequent fault-tolerant pattern mining has been widely studied. However, all of the researches focus on static database. In this paper, we propose an efficient framework to analyze the frequent FT-patterns mining in dynamic database. To avoid re-scanning the whole database, beside of keeping the fault-tolerance pattern, we will also keep the potential fault-tolerance pattern that has higher possibility of becoming a fault-tolerance pattern. The experimental results show that by re-using the existing pattern that had been generated, the proposed algorithms are highly efficient in terms of execution time and maximum memory usage for mining fault-tolerance frequent pattern in dynamic database compare to FFM algorithm.
Keywords: Item support, FT support, Fault-tolerant frequent pattern, Data mining, Dynamic database
Contributor Information
Daniel Palacios-Marqués, Email: dapamar@doe.upv.es.
Domingo Ribeiro Soriano, Email: domingo.ribeiro@uv.es.
Kun Huang Huarng, Email: khhuarng@mail.fcu.edu.tw.
Guanling lee, Email: guanling@mail.ndhu.edu.tw.
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