A Survey on Improving the Efficiency of Association Rule Mining Using FP-Growth

Data Mining is often considered as a process of automatic discovery of new knowledge from large databases. The extensive amount of knowledge and data stored in databases requires the development for storing and accessing the data, data analysis and effective use of stored knowledge of data. Association Rule Mining (ARM) is one of the important aspects in data mining, which generates large amount of itemsets in database. Many algorithms have been proposed to efficiently mine association rules. One of the most important approaches is the frequent pattern growth (FP-growth) method, which is efficient and scalable. In order to improve the efficiency of FP-growth method, this paper is surveyed on association rule mining with the aid of FP-growth algorithm in various aspects.