Performance Evaluation of Apriori on Dual Core with Multiple Threads

Abstract

The complexity of handling the scalability problem of huge data can be reduced with parallel processing. The efficiency of parallel processing changes as the number of processors or number of threads change. Parallel processing is more appropriate for the field like data mining as it is the technique of analyzing large quantities of data to extract useful knowledge. Data mining is very much essential to the modern society as more and more data is being collected from various fields. Experiments are conducted to test the run time efficiency of the apriori algorithm on dual core processor by changing the number of threads for different databases at different support counts. This paper also present the comparison of real time, user time and system time with multiple threads on dual core compared to sequential implementation.

Analysis of frequent itemset generation based on trie data structure in Apriori algorithm

“…From another perspective, it is common to use multi-thread and computing power with multicore architecture [12] in supporting data processing. This raises the suspicion that multi-thread in [13] and [14] can produce better performance also in getting this frequent itemset, as well as a challenge on how to determine the best performance process architecture that is applied to which subprocesses as threads and how big is the increase for the best multi-thread architecture in a single server environment, where an experiment in multi-node server environment was proposed by [15], [16].…”