LifeLines: visualizing personal histories
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
PlanningLines: Novel Glyphs for Representing Temporal Uncertainties and Their Evaluation
IV '05 Proceedings of the Ninth International Conference on Information Visualisation
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Aligning temporal data by sentinel events: discovering patterns in electronic health records
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Timeline trees: visualizing sequences of transactions in information hierarchies
AVI '08 Proceedings of the working conference on Advanced visual interfaces
Temporal Data Mining
LifeFlow: visualizing an overview of event sequences
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Extracting insights from temporal event sequences is an important challenge. In particular, mining frequent patterns from event sequences is a desired capability for many domains. However, most techniques for mining frequent patterns are ineffective for real-world data that may be low-resolution, concurrent, or feature many types of events, or the algorithms may produce results too complex to interpret. To address these challenges, we propose Frequence, an intelligent user interface that integrates data mining and visualization in an interactive hierarchical information exploration system for finding frequent patterns from longitudinal event sequences. Frequence features a novel frequent sequence mining algorithm to handle multiple levels-of-detail, temporal context, concurrency, and outcome analysis. Frequence also features a visual interface designed to support insights, and support exploration of patterns of the level-of-detail relevant to users. Frequence's effectiveness is demonstrated with two use cases: medical research mining event sequences from clinical records to understand the progression of a disease, and social network research using frequent sequences from Foursquare to understand the mobility of people in an urban environment.