Performance Evaluation of Sequential and Parallel Mining of Association Rules Using Apriori Algorithms

The information age has seen most of the activities generating huge volumes of data. The explosive growth of business, scientific and government databases sizes has far outpaced our ability to interpret and digest the stored data. This has created a need for new generation tools and techniques for automated and intelligent database analysis. These tools and techniques are the subjects of the rapidly emerging field of data mining. One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consuming operation in this discovery process is the computation of the frequency of the occurrences of interesting subset of items (called candidates) in the database of transactions. To prune the exponentially large space of candidates, most existing algorithms consider only those candidates that have a user defined minimum support. Even with the pruning, the task of finding all association rules requires a lot of computation power and memory. Parallel computers offer a potential solution to the computation requirement of this task, provided efficient and scalable parallel algorithms can be designed. In this paper, we have implemented Sequential and Parallel mining of Association Rules using Apriori algorithms and evaluated the performance of both algorithms.