Rule Mining for Dynamic Databases

Association rules identify associations among data items and were introduced in  [1]. A detailed discussion on association rules can be found in [2], [8]. One important step in Association rule mining is to find frequent itemsets. Most of the algorithms to find frequent itemsets deal with the static...

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Bibliographic Details
Published inDistributed Computing - IWDC 2004 pp. 46 - 51
Main Authors Das, A, Bhattacharyya, D K
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 01.01.2004
SeriesLecture Notes in Computer Science
Subjects
border
candidate sets
confidence
frequent items
itemsets
promoted border
Rule mining
support
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ISBN9783540240761
3540240764
ISSN0302-9743
1611-3349
DOI10.1007/978-3-540-30536-1_6

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Summary:Association rules identify associations among data items and were introduced in  [1]. A detailed discussion on association rules can be found in [2], [8]. One important step in Association rule mining is to find frequent itemsets. Most of the algorithms to find frequent itemsets deal with the static databases. There are very few algorithms that deal with dynamic(incremental) databases. The most classical algorithm to find frequent itemsets in dynamic database is Borders algorithm [7]. But the Borders algorithm is suitable for centralized databases. This paper presents a modified version of the Borders algorithm, called Distributed Borders, which is suitable for Distributed Dynamic databases.
ISBN:9783540240761
3540240764
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-540-30536-1_6