Privacy-preserving data mining
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IEEE Transactions on Knowledge and Data Engineering
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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
Learning Stochastic Regular Grammars by Means of a State Merging Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
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International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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STOC '84 Proceedings of the sixteenth annual ACM symposium on Theory of computing
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
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Preserving Privacy by De-Identifying Face Images
IEEE Transactions on Knowledge and Data Engineering
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ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining evolving data streams for frequent patterns
Pattern Recognition
Discovering Significant Patterns
Machine Learning
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
A bibliographical study of grammatical inference
Pattern Recognition
Discovering Patterns in Flows: A Privacy Preserving Approach with the ACSM Prototype
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Significant motifs in time series
Statistical Analysis and Data Mining
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During the past decade, sequential pattern mining has been the core of numerous research efforts. It is now possible to efficiently extract knowledge of users' behavior from a huge set of sequences collected over time. This has applications in various domains such as purchases in supermarkets, Web site visits, etc. However, sequence mining algorithms do little to control the risks of extracting false discoveries or overlooking true knowledge. In this paper, the theoretical conditions to achieve a relevant sequence mining process are examined. Then, the article offers a statistical view of sequence mining which has the following advantages: First, it uses a compact and generalized representation of the original sequences in the form of a probabilistic automaton. Second, it integrates statistical constraints to guarantee the extraction of significant patterns. Finally, it provides an interesting solution in a privacy preserving context in order to respect individuals' information. An application in car flow modeling is presented, showing the ability of our algorithm (acsm) to discover frequent routes without any private information. Comparisons with a classical sequence mining algorithm (spam) are made, showing the effectiveness of our approach.