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Depth-first frequent itemset mining in relational databases

Published: 13 March 2005 Publication History

Abstract

Data mining on large relational databases has gained popularity and its significance is well recognized. However, the performance of SQL based data mining is known to fall behind specialized implementation since the prohibitive nature of the cost associated with extracting knowledge, as well as the lack of suitable declarative query language support. We investigate approaches based on SQL for the problem of finding frequent patterns from a transaction table, including an algorithm that we recently proposed, called Propad (PRO-jection PAttern Discovery). Propad fundamentally differs from an Apriori-like candidate set generation-and-test approach. This approach successively projects the transaction table into frequent itemsets to avoid making multiple passes over the large original transaction table and generating a huge sets of candidates. We have made performance evaluation on DBMS (IBM DB2 UDB EEE V8) and compared the performance results with K-Way join approach proposed in [11] and SQL based FP-tree approach proposed in [13]. The experimental results show that our algorithm can get efficient performance.

References

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  • (2017)Tree-based frequent itemsets mining for analysis of life-satisfaction and loneliness of retired athletesCluster Computing10.1007/s10586-017-1080-420:4(3327-3335)Online publication date: 1-Dec-2017
  • (2015)Vertical Association Rule Mining: Case study implementation with relational DBMS2015 International Symposium on Technology Management and Emerging Technologies (ISTMET)10.1109/ISTMET.2015.7359044(279-284)Online publication date: Aug-2015
  • (2015)Assessing the Suitability of In-Memory Databases in an Enterprise ContextProceedings of the 2015 International Conference on Enterprise Systems10.1109/ES.2015.15(78-89)Online publication date: 14-Oct-2015
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    cover image ACM Conferences
    SAC '05: Proceedings of the 2005 ACM symposium on Applied computing
    March 2005
    1814 pages
    ISBN:1581139640
    DOI:10.1145/1066677
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    Publication History

    Published: 13 March 2005

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    Author Tags

    1. SQL based mining algorithms
    2. data mining
    3. database mining
    4. frequent pattern mining
    5. mining algorithms in SQL

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    March 13 - 17, 2005
    New Mexico, Santa Fe

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    View all
    • (2017)Tree-based frequent itemsets mining for analysis of life-satisfaction and loneliness of retired athletesCluster Computing10.1007/s10586-017-1080-420:4(3327-3335)Online publication date: 1-Dec-2017
    • (2015)Vertical Association Rule Mining: Case study implementation with relational DBMS2015 International Symposium on Technology Management and Emerging Technologies (ISTMET)10.1109/ISTMET.2015.7359044(279-284)Online publication date: Aug-2015
    • (2015)Assessing the Suitability of In-Memory Databases in an Enterprise ContextProceedings of the 2015 International Conference on Enterprise Systems10.1109/ES.2015.15(78-89)Online publication date: 14-Oct-2015
    • (2007)Processing sequential patterns in relational databasesJournal on data semantics VIII10.5555/1768269.1768282(203-217)Online publication date: 1-Jan-2007
    • (2007)Design and Implementation of Intranet Security Audit System Based on Load BalancingProceedings of the 2007 IEEE International Conference on Granular Computing10.1109/GRC.2007.56Online publication date: 2-Nov-2007
    • (2007)Mining Long, Sharable Patterns in Trajectories of Moving ObjectsGeoInformatica10.1007/s10707-007-0042-z13:1(27-55)Online publication date: 4-Dec-2007
    • (2007)Processing Sequential Patterns in Relational DatabasesJournal on Data Semantics VIII10.1007/978-3-540-70664-9_8(203-217)Online publication date: 2007
    • (2005)Processing sequential patterns in relational databasesProceedings of the 7th international conference on Data Warehousing and Knowledge Discovery10.1007/11546849_43(438-447)Online publication date: 22-Aug-2005
    • (2005)Frequent itemset mining with parallel RDBMSProceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining10.1007/11430919_63(539-544)Online publication date: 18-May-2005

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