Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
LitLinker: capturing connections across the biomedical literature
Proceedings of the 2nd international conference on Knowledge capture
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Literature mining method RaJoLink for uncovering relations between biomedical concepts
Journal of Biomedical Informatics
On exploiting the power of time in data mining
ACM SIGKDD Explorations Newsletter
Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
International Journal of Intelligent Systems - Granular Computing: Models and Applications
Supporting creativity: towards associative discovery of new insights
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Selecting the links in bisonets generated from document collections
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
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Bisociations represent interesting relationships between seemingly unconnected concepts from two or more contexts. Most of the existing approaches that permit the discovery of bisociations from data rely on the assumption that contexts are static or considered as unchangeable domains. Actually, several real-world domains are intrinsically dynamic and can change over time. The same domain can change and can become completely different from what/how it was before: a dynamic domain observed at different time-points can present different representations and can be reasonably assimilated to a series of distinct static domains. In this work, we investigate the task of linking concepts from a dynamic domain through the discovery of bisociations which link concepts over time. This provides us with a means to unearth linkages which have not been discovered when observing the domain as static, but which may have developed over time, when considering the dynamic nature. We propose a computational solution which, assuming a time interval-based discretization of the domain, explores the spaces of association rules mined in the intervals and chains the rules on the basis of the concept generalization and information theory criteria. The application to the literature-based discovery shows how the method can rediscover known connections in biomedical terminology. Experiments and comparisons using alternative techniques highlight the additional peculiarities of this work.