Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
TSP: Mining Top-K Closed Sequential Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
A novel Boolean algebraic framework for association and pattern mining
WSEAS Transactions on Computers
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Sequential pattern mining is a heavily researched area in the field of data mining with wide variety of applications. The task of discovering frequent sequences is challenging, because the algorithm needs to process a combinatorially explosive number of possible sequences. Most of the methods dealing with the sequential pattern mining problem are based on the approach of the traditional task of itemset mining, because the former can be interpreted as the generalization of the latter. Several algorithms use a level-wise "candidate generate and test" approach, while others use projected databases to discover the frequent sequences. In this paper a classification of the well-known sequence mining algorithm is presented. Because each algorithm has its own advantages and drawbacks regarding the execution time and the memory requirements, and the exact aim of the algorithms differs as well, thus an exact ranking of the methods is omitted. A basic level-wise algorithm, the GSP is described in detail. Because the level-wise algorithms need less memory in general than the projection-based ones, an efficient implementation of the GSP algorithm is also suggested. Two novel methods, the Bitmap-based GSP (BGSP) and the SM-Tree (State Machine-Tree) algorithms are presented as an enhancement of the GSP-based sequential pattern mining approach.