Combinatorial pattern discovery for scientific data: some preliminary results
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
PODS '95 Proceedings of the fourteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
The Representation Race - Preprocessing for Handling Time Phenomena
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Dynamic Discretization of Continuous Values from Time Series
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Identifying and Using Patterns in Sequential Data
ALT '93 Proceedings of the 4th International Workshop on Algorithmic Learning Theory
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
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We present an integrated methodology for the discovery of hidden relations and underlying indicative patterns in time-series collections. The methodology is realized by the smooch integration of: (i) dynamic and qualitative discretization of time-series data, (ii) matching time-series by respective similarity assessment operations, and (iii) a novel hierarchical clustering process, grounded on a graph-theoretic technique, which combines information about the distances between objects and their respective feature-based descriptions. We apply our methodology on in-vivo neuropsychological data targeting the challenging task of patterning brain-developmental events.