Performance evaluation of frequent pattern mining algorithms using web log data for web usage mining

In today's information era, the Internet is a powerful platform as the data repository that plays a great role in storing, sharing, and retrieve information for knowledge discovery. However, as there are countless, dynamic, and significant growth of data, web users face big problems in terms of the relevant information required. Consequently, poor information precision and retrieval are part of the hottest recent research areas in today's world. Despite the voluminous of information resided on the web, valuable informative knowledge could possibly be discovered with the application of advanced data mining techniques. Association rule mining, as a technique in data mining, is one way to discover frequent patterns from various data sources. In this paper, three of the foremost association rule mining algorithms used for frequent pattern discovering namely, Eclat, Apriori, and FP-Growth examined on three sets of transactional databases devised from server access log file. The comparison is made both in execution time and memory usage aspects. Unlike most previous research works, findings, in this paper, reveal that each of the algorithms has their own appropriateness and specificities that can best fit depending on the data size and support parameter thresholds.