Set-oriented data mining in relational databases

Houtsma, Maurice A. W.; Swami, Arun

doi:10.1016/0169-023x(95)00024-m

Cited by 35 publications

(12 citation statements)

References 17 publications

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“…The first attempt to the particular problem of integrated frequent item set mining was the SETM algorithm [10,17], expressed as SQL queries working on relational tables. The Apriori algorithm [1] opened up new prospects for FIM.…”

Section: F Sql-based Algorithmsmentioning

confidence: 99%

Comparative Analysis of Various Approaches Used in Frequent Pattern Mining

Garg¹,

Sharma²

2011

SpecialIssue

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Section: F Sql-based Algorithmsmentioning

confidence: 99%

Comparative Analysis of Various Approaches Used in Frequent Pattern Mining

Garg¹,

Sharma²

2011

SpecialIssue

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“…The SETM algorithm is the first SQL-based approach [10] described in the literature. Subsequent work suggested improvements of SETM.…”

Section: Related Workmentioning

confidence: 99%

Processing frequent itemset discovery queries by division and set containment join operators

Rantzau

2003

Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery

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SQL-based data mining algorithms are rarely used in practice today. Most performance experiments have shown that SQL-based approaches are inferior to main-memory algorithms. Nevertheless, database vendors try to integrate analysis functionalities to some extent into their query execution and optimization components in order to narrow the gap between data and processing. Such a database support is particularly important when data mining applications need to analyze very large datasets or when they need access current data, not a possibly outdated copy of it. We investigate approaches based on SQL for the problem of finding frequent itemsets in a transaction table, including an algorithm that we recently proposed, called Quiver, which employs universal and existential quantifications. This approach employs a table schema for itemsets that is similar to the commonly used vertical layout for transactions: each item of an itemset is stored in a separate row. We argue that expressing the frequent itemset discovery problem using quantifications offers interesting opportunities to process such queries using set containment join or set containment division operators, which are not yet available in commercial database systems. Initial performance experiments reveal that Quiver cannot be processed efficiently by commercial DBMS. However, our experiments with query execution plans that use operators realizing set containment tests suggest that an efficient processing of Quiver is possible.

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“…Mining association rules, an important subfield of data mining, was firstly introduced by Agrawal et al [1]. So far, most of the research has been focused on designing fast algorithms for it [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][19][20][21][22][23][24][25][26][27][28][29]. The problem of mining all association rules can be decomposed into two sub problems.…”

Section: Introductionmentioning

confidence: 99%

Share-Inherit: A novel approach for mining frequent patterns

Lin

Zhu

2010

2010 8th World Congress on Intelligent Control and Automation

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Mining frequent patterns has attracted considerable attention in the data mining field. Most of the current studies adopt the pattern growth approach of divide-andconquer. However, as the mining process is completely split into parts, all relevant algorithms still encounter some performance bottlenecks. In this study, we propose a new data structure, Share-struct, which is derived but obviously different from FPtree. Then we developed an efficient algorithm, Share-Inherit, for mining all frequent patterns. Based on the Share-struct, Share-Inherit provides a way to share most of the results from the previous mining process instead of separating them distinctively, thereby dramatically reducing the cost of traversing FP-tree and significantly improving the performance of algorithms based on pattern growth. Furthermore, various optimization techniques used in Share-Inherit sufficiently improve the algorithm. The experimental results show that Share-Inherit algorithm not only performs better than the existing algorithms using pattern growth, but also improves them by incorporating Share-Inherit strategy.Index Terms -data mining, association rules mining, frequent patterns mining, pattern growth, share-inherit.

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Set-oriented data mining in relational databases

Cited by 35 publications

References 17 publications

Comparative Analysis of Various Approaches Used in Frequent Pattern Mining

Comparative Analysis of Various Approaches Used in Frequent Pattern Mining

Processing frequent itemset discovery queries by division and set containment join operators

Share-Inherit: A novel approach for mining frequent patterns

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