Data Mining In Time Series Databases (series In Machine Perception And Artificial Intelligence)
by Horst Bunke /
2004 / English / PDF
3.1 MB Download
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 manual examines
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 text 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.
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 manual examines
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 text 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.
