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Fault Tolerance Patterns Mining in Dynamic Databases

  • Delvi Ester
  • Guanling leeEmail author
Conference paper
  • 721 Downloads
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 222)

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 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Dong Hwa UniversityShoufengTaiwan, R.O.C.

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