Frequent Itemsets Mining: An Efficient Graphical Approach

Recent advances in computer technology in terms of speed, cost, tremendous amount of computing power and decrease data processing time has spurred increased interest in data mining applications to extract useful knowledge from data. Over the last couple of years, data mining technology has been successfully employed to various business domains and scientific areas. Various data mining techniques are now available and data mining software has become more matured in recent years. Discovering association rules that identify relationships among sets of items is an important problem in data mining. Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore it has attracted significant research attention. The approach used in this paper uses a hashing technique to generate a candidate set of large 2-itemsets, directed graphs are formed using the support of 2-itemsets as a result generating all possible frequent k-itemsets in the database.