Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh 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
An Efficient Single-Scan Algorithm for Mining Essential Jumping Emerging Patterns for Classification
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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Frequent pattern mining plays an essential role in many important data mining tasks. FP-Growth, an algorithm which mines frequent patterns in the frequent pattern tree (FP-tree), is very efficient. However, it still encounters performance bottlenecks when creating conditional FP-trees recursively during the mining process. In this work, we propose a new algorithm, called Maximum-Item-First Pattern Growth (MIFPG), for mining frequent patterns. MIFPG searches the FP-tree in the depth-first, top-down manner, as opposed to the bottom-up order of FP-Growth. Its key idea is that maximum items are always considered first when the current pattern grows. In this way, no concrete realization of conditional pattern bases is needed and the major operations of mining are counting and link adjusting, which are usually inexpensive. Experiments show that, in comparison with FP-Growth, our algorithm is about three times faster and consumes less memory space; it also has good time and space scalability with the number of transactions.