Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Applied Data Mining for Business and Industry
Applied Data Mining for Business and Industry
A new approach for problem of sequential pattern mining
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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This paper addresses the discovery of sequential patterns in very large databases. Most of the existing algorithms use lattice structures in the space search that are very demanding computationally. The output of these algorithms generates a large number of rules. The aim of this work is to create a swift algorithm for the discovery of sequential patterns with a low time complexity. In this work, we also want to define tools that allow us to simplify the work of the final user, by offering a new visualization of the sequences, while bypassing the analysis of thousands of association rules.