Data Mining In Time Series Databases
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| Mark Last, Abraham Kandel, Horst Bunke | ||||
| Series in machine perception and artificial intelligence v.57 | ||||
| World Scientific | ||||
| 2004 | ||||
| English | ||||
| 205 pages | ||||
| 3.06 MB | ||||
[tab] [content title="Description"]Adding the time dimension to real-world databases produces TimeSeries Databases (TSDB) and introduces new aspects and difficultiesto data mining and knowledge discovery. This book covers thestate-of-the-art methodology for mining time series databases. Thenovel data mining methods presented in the book include techniquesfor efficient segmentation, indexing, and classification of noisy anddynamic time series. A graph-based method for anomaly detection intime series is described and the book also studies the implicationsof a novel and potentially useful representation of time series asstrings. The problem of detecting changes in data mining models thatare induced from temporal databases is additionally discussed. [/content] [content title="Content"] [/content] [content title="About the author"]Abraham Kandel is a academic researcher from University of South Florida. The author has contributed to research in topic(s): Fuzzy logic & Fuzzy set operations. The author has an hindex of 48, co-authored 335 publication(s) receiving 10490 citation(s). Previous affiliations of Abraham Kandel include Tel Aviv University & Florida State University. [/content] [/tab]
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