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Efficient mining and maintenance of association rules in large datasets


Efficient mining and maintenance of association rules in large datasets

Song, Yu (2005) Efficient mining and maintenance of association rules in large datasets. Masters thesis, Concordia University.

Text (application/pdf)
MR04450.pdf - Accepted Version


Data mining is the exploration and analysis of large quantities of data to discover meaningful patterns and rules. Mining frequent itemsets plays an essential role in many data mining tasks, which attempts to find interesting associations or correlations among a large set of data items. Efficient discovery of frequent large itemsets and its dual problem of mining association rules are well studied and efficient solution techniques have been developed and deployed in data analysis and mining tools. When new transactions are added to the dataset, it is important to maintain such discovered patterns and rules without requiring processing the whole dataset and re-computing from scratch. In this research, we first focus on the maintenance problem and propose an in-memory technique to identify frequent large itemsets when the data set grows by addition of new transactions. The basic solution idea is to identify and use negative borders for maintenance. We then use this idea and develop a divide-and-conquer technique, based on partitioning , to compute frequent itemsets in large datasets, which do not fit into the main memory. Our experimental results show that the proposed techniques are efficient and scalable.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Song, Yu
Pagination:viii, 88 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science and Software Engineering
Thesis Supervisor(s):Alagar, Vangalur and Shiri, Nematollaah
ID Code:8419
Deposited By: Concordia University Library
Deposited On:18 Aug 2011 18:24
Last Modified:18 Jan 2018 17:33
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