CCE Theses and Dissertations

Date of Award


Document Type


Degree Name

Doctor of Information Science


Graduate School of Computer and Information Sciences


Junping Sun

Committee Member

Michael Laszlo

Committee Member

James Cannady


Apriori, Association rules, Data mining, Distributed algorithms, Lattice, Parallel systems


The discovery of interesting patterns from database transactions is one of the major problems in knowledge discovery in database. One such interesting pattern is the association rules extracted from these transactions. Parallel algorithms are required for the mining of association rules due to the very large databases used to store the transactions. In this paper we present a parallel algorithm for the mining of association rules. We implemented a parallel algorithm that used a lattice approach for mining association rules. The Dynamic Distributed Rule Mining (DDRM) is a lattice-based algorithm that partitions the lattice into sublattices to be assigned to processors for processing and identification of frequent itemsets. Experimental results show that DDRM utilizes the processors efficiently and performed better than the prefix-based and partition algorithms that use a static approach to assign classes to the processors. The DDRM algorithm scales well and shows good speedup.

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