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
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
The Representation Race - Preprocessing for Handling Time Phenomena
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
IEEE Transactions on Computers
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We present, Mine Time, a tool that supports discovery over time series data. Mine Time is realized by the introduction of novel algorithmic processes, which support assessment of coherence and similarity across timeseries data. The innovation comes from the inclusion of specific ‘control' operations in the elaborated time-series matching metric. The final outcome is the clustering of time-series into similar-groups. Clustering is performed via the appropriate customization of a phylogeny-based clustering algorithm and tool. We demonstrate Mine Time via two experiments.