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Mining fault-tolerant frequent patterns efficiently with powerful pruning

Published: 16 March 2008 Publication History

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, 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.

References

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cover image ACM Conferences
SAC '08: Proceedings of the 2008 ACM symposium on Applied computing
March 2008
2586 pages
ISBN:9781595937537
DOI:10.1145/1363686
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 16 March 2008

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Author Tags

  1. FT support
  2. FT-association graph
  3. data mining
  4. fault-tolerant frequent pattern
  5. item support

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SAC '08
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SAC '08: The 2008 ACM Symposium on Applied Computing
March 16 - 20, 2008
Fortaleza, Ceara, Brazil

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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  • (2018)On mining approximate and exact fault-tolerant frequent itemsetsKnowledge and Information Systems10.1007/s10115-017-1079-455:2(361-391)Online publication date: 1-May-2018
  • (2015)Fault Tolerance Patterns Mining in Dynamic DatabasesNew Information and Communication Technologies for Knowledge Management in Organizations10.1007/978-3-319-22204-2_12(122-130)Online publication date: 14-Jul-2015
  • (2014)On Mining Proportional Fault-Tolerant Frequent ItemsetsDatabase Systems for Advanced Applications10.1007/978-3-319-05810-8_23(342-356)Online publication date: 2014
  • (2011)Frequent itemset mining of uncertain data streams using the damped window modelProceedings of the 2011 ACM Symposium on Applied Computing10.1145/1982185.1982393(950-955)Online publication date: 21-Mar-2011
  • (2010)Mining uncertain data for frequent itemsets that satisfy aggregate constraintsProceedings of the 2010 ACM Symposium on Applied Computing10.1145/1774088.1774305(1034-1038)Online publication date: 22-Mar-2010

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