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
Multi-dimensional sequential pattern mining
Proceedings of the tenth international conference on Information and knowledge management
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EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Similarity Search for Multidimensional Data Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Mining context based sequential patterns
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
A Case Study in Sequential Pattern Mining for IT-Operational Risk
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Mining multidimensional and multilevel sequential patterns
ACM Transactions on Knowledge Discovery from Data (TKDD)
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This paper describes practical aspects of a novel approach to the sequential pattern mining named Context Based Sequential Pattern Mining (CBSPM). It introduces a novel ContextMapping algorithm used for the context pattern mining and an illustrative example showing some advantages of the proposed method. The approach presented here takes into consideration some shortcomings of the classic problem of the sequential pattern mining. The significant advantage of the classic sequential patterns mining is simplicity. It introduces simple element construction, built upon set of atomic items. The comparison of sequence's elements utilizes simple inclusion of sets. But many practical problems like web event mining, monitoring, tracking and rules generation often require mining more complex data. The CBSPM takes into account non nominal attributes and similarity of sequence's elements. An approach described here extends traditional problem adding a vector of context attributes of any kind to sequences and sequences elements. Context vectors contain details about sequence's and element's origin. The mining process results in context patterns containing additional, valuable context information useful in interpretation of patterns origin.