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
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent Pattern Mining on Message Passing Multiprocessor Systems
Distributed and Parallel Databases
Mining lossless closed frequent patterns with weight constraints
Knowledge-Based Systems
Toward terabyte pattern mining: an architecture-conscious solution
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
BitTableFI: An efficient mining frequent itemsets algorithm
Knowledge-Based Systems
Tree partition based parallel frequent pattern mining on shared memory systems
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Memory-efficient frequent-itemset mining
Proceedings of the 14th International Conference on Extending Database Technology
Efficient colossal pattern mining in high dimensional datasets
Knowledge-Based Systems
Load balancing approach parallel algorithm for frequent pattern mining
PaCT'07 Proceedings of the 9th international conference on Parallel Computing Technologies
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Since extraction of frequent itemsets from a transaction database is crucial to several data mining tasks such as association rule generation, so frequent itemset mining is one of the most important concepts in data mining. One of the major problems in frequent itemset mining is the explosion of the number of results which is directly effecting on the execution time of itemset mining algorithms. To address this problem, closed itemsets have been proposed, which provides concise lossless representations of the original collection of frequent itemsets. Henceforth, the frequencies of all itemsets in the original collection can be reconstructed from the reduced collection. However, the reduction provided by this exact method is not sufficient to solve the pattern explosion problem, mainly because of high dimensional datasets which have large number of items in each transaction. Colossal itemset mining is another solution to reduce the output size which will not be useful if the set of all frequent itemsets have been required. Higher level of performance improvement can be obtained from efficient scalable parallel mining methods. In this paper we represent an efficient scalable parallel algorithm using systolic arrays to conduct mining of frequent itemsets in very large, such as high dimensional, datasets. In our algorithm, we use a bit matrix to compress the dataset and mapping the mining algorithm on the systolic arrays architecture. For this purpose, each transaction of dataset represents as a row in the bit matrix. We use this bit matrix structure to model the pattern mining as a systolic array problem. Our experimental results and performance study show that this algorithm outperforms substantially the best previously developed parallel algorithms.