Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
TreeITL-Mine: Mining Frequent Itemsets Using Pattern Growth, Tid Intersection, and Prefix Tree
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Mining frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of long frequent patterns from very large dense datasets
Design and application of hybrid intelligent systems
ACM Computing Surveys (CSUR)
ON DATA STRUCTURES FOR ASSOCIATION RULE DISCOVERY
Applied Artificial Intelligence
Memory-efficient frequent-itemset mining
Proceedings of the 14th International Conference on Extending Database Technology
An integrated approach for mining meta-rules
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Profile association rule mining using tests of hypotheses without support threshold
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
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Discovering association rules that identify relationships among sets of items is an important problem in data mining. Finding frequent item sets is computationally the most expensive step in association rule discovery and therefore it has attracted significant research attention. In this paper, we present a more efficient algorithm for mining complete sets of frequent item sets. In designing our algorithm, we have modified and synthesized a number of useful ideas that include prefix trees, pattern-growth, and tid-intersection. We extend the prefix-tree structure to store transaction groups and propose a new method to compress the tree. Transaction-id intersection is modified to include the count of transaction groups. We present performance comparisons of our algorithm against the fastest Apriori algorithm, Eclat and the latest extension of FP-Growth known as OpportuneProject. To study the trade-offs in compressing transactions in the prefix tree, we compare the performance of our algorithm with and without using the modified compressed prefix tree. We have tested all the algorithms using several widely used test datasets. The performance study shows that the new algorithm significantly reduces the processing time for mining frequent item sets from dense data sets that contain relatively long patterns. We discuss the performance results in detail and also the strengths and limitations of our algorithm.