CCE Theses and Dissertations

A Comparative Study of the Effectiveness of Three Models of Distance Education on Student Achievement and Level of Satisfaction

Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Graduate School of Computer and Information Sciences


Steven R. Terrell

Committee Member

Laurie Dringus

Committee Member

Jeanann S. Boyce


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. The goal of this research was to develop and implement a parallel algorithm for mining 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 sub lattices to be assigned to processors for processing and identification of frequent item sets. We implemented the DDRM using a dynamic load balancing approach to assign classes to processors for analysis of these classes in order to determine if there are any rules present in them. Parallel algorithms are required for the mining of association rules due to the very large databases used to store the transactions. Some of the previous parallel algorithms are Count Distribution (CD), Data Distribution (DD), Candidate Distribution (CDD), Intelligent Data Distribution (IDD), and Hybrid Distribution (HD). However the costs associated with these algorithms are hash tree construction, hash tree traversal, communication overhead, input/output (I/O) cost and data movement respectively. These algorithms assign tasks to the processors using a static scheduling scheme. The main challenge for a static scheduling scheme is to determine the amount of time that will be needed to process each task. This information can then be used to compute the total time needed to process all the tasks and to divide these tasks among the processors so that an equal amount of tasks are assigned to each processor using processing time as the unit of measurement. 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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