Non Recursive Generation of Frequent K-itemsets from Frequent Pattern Tree Representations Mohammad El-Hajj and Osmar R. Za¨ ıane Department of Computing Science, University of Alberta, Edmonton AB, Canada {mohammad, zaiane}@cs.ualberta.ca Abstract. Existing association rule mining algorithms suffer from many problems when mining massive transactional datasets. One major prob- lem is the high memory dependency: gigantic data structures built are assumed to fit in main memory; in addition, the recursive mining pro- cess to mine these structures is also too voracious in memory resources. This paper proposes a new association rule-mining algorithm based on frequent pattern tree data structure. Our algorithm does not use much more memory over and above the memory used by the data structure. For each frequent item, a relatively small independent tree called COFI-tree, is built summarizing co-occurrences. Finally, a simple and non-recursive mining process mines the COFI-trees. Experimental studies reveal that our approach is efficient and allows the mining of larger datasets than those limited by FP-Tree 1 Introduction Recent days have witnessed an explosive growth in generating data in all fields of science, business, medicine, military, etc. The same rate of growth in the pro- cessing power of evaluating and analyzing the data did not follow this massive growth. Due to this phenomenon, a tremendous volume of data is still kept with- out being studied. Data mining, a research field that tries to ease this problem, proposes some solutions for the extraction of significant and potentially useful patterns from these large collections of data. One of the canonical tasks in data mining is the discovery of association rules. Discovering association rules, con- sidered as one of the most important tasks, has been the focus of many studies in the last few years. Many solutions have been proposed using a sequential or parallel paradigm. However, the existing algorithms depend heavily on massive computation that might cause high dependency on the memory size or repeated I/O scans for the data sets. Association rule mining algorithms currently pro- posed in the literature are not sufficient for extremely large datasets and new solutions, that especially are less reliant on memory size, still have to be found. 1.1 Problem Statement The problem consists of finding associations between items or itemsets in trans- actional data. The data could be retail sales in the form of customer transactions