SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Evidence Theory and Its Applications
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Evidence Theory and Its Applications
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Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Feature selection for high-dimensional genomic microarray data
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PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Sequential PAttern mining using a bitmap representation
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Dynamic query tools for time series data sets: timebox widgets for interactive exploration
Information Visualization
On the discovery of significant statistical quantitative rules
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Periodicity Detection in Time Series Databases
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Concepts and Techniques
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A Unifying Framework for Detecting Outliers and Change Points from Time Series
IEEE Transactions on Knowledge and Data Engineering
Pattern Discovery of Fuzzy Time Series for Financial Prediction
IEEE Transactions on Knowledge and Data Engineering
On compressing frequent patterns
Data & Knowledge Engineering
Constraint-based sequential pattern mining: the pattern-growth methods
Journal of Intelligent Information Systems
Mining contiguous sequential patterns from web logs
Proceedings of the 16th international conference on World Wide Web
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Efficient Discovery of Frequent Approximate Sequential Patterns
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
On effective presentation of graph patterns: a structural representative approach
Proceedings of the 17th ACM conference on Information and knowledge management
Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining
Journal of Intelligent Information Systems
A New Algorithm for Mining Sequential Patterns
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Stream Sequential Pattern Mining with Precise Error Bounds
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Pattern Discovery in Bioinformatics: Theory & Algorithms
Pattern Discovery in Bioinformatics: Theory & Algorithms
Efficient Mining of Closed Repetitive Gapped Subsequences from a Sequence Database
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Efficient discovery of unusual patterns in time series
New Generation Computing
Java-ML: A Machine Learning Library
The Journal of Machine Learning Research
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Mining sequential patterns is an important activity in computerized data analysis, and further analysis of discovered patterns can lead to more important findings in a post mining stage. Methods that integrate the traditional mining tasks with a knowledge representation facilitating such as post pruning are therefore needed. In this paper a set-based approach to mining frequent sequential patterns in customer transactional databases is described which includes an extensible knowledge representation. This knowledge representation is a byproduct of the set-based approach which contributes to facilitate post data mining and analysis. The proposed approach employs a set based knowledge representation and improves the performance of Apriori based algorithms while preserving a complete set of sequential patterns. It takes advantage of an incremental mining methodology and provides a rich knowledge representation. Performance results of the proposed approach are compared to the performance of existing sequential pattern mining algorithms including GSP Generalized Sequential Pattern and PrefixSpan. The effective knowledge representation inferred from the set based approach can be extended to other data mining tasks and data analysis models. Such extension is demonstrated by two instances of enriched knowledge representations in sequential databases, namely Set Occurrence Tables and Set Distance computations, along with their use of association rules generation, feature selection and ad hoc analysis in the post mining stage.