ADDING THE TIME DIMENSION TO REAL-WORLD DATABASES PRODUCES TIME SERIES DATABASES (TSDB) AND INTRODUCES NEW ASPECTS AND DIFFICULTIES TO DATA MINING AND KNOWLEDGE DISCOVERY. THIS BOOK COVERS THE STATE-OF-THE-ART METHODOLOGY FOR MINING TIME SERIES DATABASES. THE NOVEL DATA MINING METHODS PRESENTED IN THE BOOK INCLUDE TECHNIQUES FOR EFFICIENT SEGMENTATION, INDEXING, AND CLASSIFICATION OF NOISY AND DYNAMIC TIME SERIES. A GRAPH-BASED METHOD FOR ANOMALY DETECTION IN TIME SERIES IS DESCRIBED AND THE BOOK ALSO STUDIES THE IMPLICATIONS OF A NOVEL AND POTENTIALLY USEFUL REPRESENTATION OF TIME SERIES AS STRINGS. THE PROBLEM OF DETECTING CHANGES IN DATA MINING MODELS THAT ARE INDUCED FROM TEMPORAL DATABASES IS ADDITIONALLY DISCUSSED.
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Adding the time dimension to real-world databases produces Time Series Databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. This book covers the state-of-the-art methodology for mining time series databases. The novel data mining methods presented in the book include techniques for efficient segmentation, indexing, and classification of noisy and dynamic time series. A graph-based method for anomaly detection in time series is described and the book also studies the implications of a novel and potentially useful representation of time series as strings. The problem of detecting changes in data mining models that are induced from temporal databases is additionally discussed.
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