Book ChapterDOI
Efficient Mining of Association Rules in Large Dynamic Databases
Edward Omiecinski,Ashoka Savasere +1 more
- pp 49-63
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TLDR
This paper presents an efficient algorithm for mining association rules within the context of a dynamic database, (i.e., a database where transactions can be added) and is an extension of the Partition algorithm which was shown to reduce the I/O overhead significantly as well as to lower the CPU overhead for most cases when compared with the performance of one of the best existing association mining algorithms.Abstract:
Mining for association rules between items in a large database of sales transactions is an important database mining problem. However, the algorithms previously reported in the literature apply only to static databases. That is, when more transactions are added, the mining process must start all over again, without taking advantage of the previous execution and results of the mining algorithm. In this paper we present an efficient algorithm for mining association rules within the context of a dynamic database, (i.e., a database where transactions can be added). It is an extension of our Partition algorithm which was shown to reduce the I/O overhead significantly as well as to lower the CPU overhead for most cases when compared with the performance of one of the best existing association mining algorithms.read more
Citations
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Proceedings ArticleDOI
An efficient algorithm to update large itemsets with early pruning
TL;DR: UWEP employs a dynamic lookahead strategy in updating the existing large itemsets by detecting and removing those that will no longer remain large after the contribution of the new set of transactions.
Book ChapterDOI
Efficient Algorithms for Incremental Update of Frequent Sequences
TL;DR: It is shown that GSP+ and MFS+ effectively reduce the CPU costs of their counterparts with only a small or even negative additional expense on I/O cost.
Journal Article
Efficient monitoring of patterns in data mining environments
TL;DR: In this paper, a general framework for monitoring patterns and detecting interesting changes without continuously mining the data is introduced, which is based on a temporal representation for patterns, in which both the content and the statistics of a pattern are modeled.
Journal Article
Monitoring the evolution of Web usage patterns
Steffan Baron,Myra Spiliopoulou +1 more
TL;DR: PAM as discussed by the authors is an automated pattern monitor, which can be used to observe changes to the behaviour of a web site's visitors, based on a temporal representation of rules in which both the content of the rule and its statistical properties are modelled.
Book ChapterDOI
Efficient Monitoring of Patterns in Data Mining Environments
TL;DR: A general framework for monitoring patterns and detecting interesting changes without continuously mining the data is introduced and it is shown that a minimal set of patterns reflecting the invariant properties of the dataset can be identified, and that interesting changes to the population can be recognized indirectly by monitoring a subset of the patterns found in the first phase.
References
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Proceedings ArticleDOI
Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Proceedings Article
Fast Algorithms for Mining Association Rules in Large Databases
Proceedings Article
An Efficient Algorithm for Mining Association Rules in Large Databases
TL;DR: This paper presents an efficient algorithm for mining association rules that is fundamentally different from known algorithms and not only reduces the I/O overhead significantly but also has lower CPU overhead for most cases.
Proceedings ArticleDOI
An effective hash-based algorithm for mining association rules
TL;DR: The number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck, and allows us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly.
Proceedings Article
Discovery of Multiple-Level Association Rules from Large Databases
Jiawei Han,Yongjian Fu +1 more
TL;DR: A top-down progressive deepening method is developed for mining multiplelevel association rules from large transaction databases by extension of some existing association rule mining techniques.