Data Mining In Time Series Databases

By: Mark Last (Editor), Abraham Kandel (Editor), Horst Bunke (Editor)

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Hardcover | 29 June 2004

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Hardcover
204 Pages

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23.5 x 15.88 x 1.91

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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. Contents: A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences (H M Lie); Indexing of Compressed Time Series (E Fink & K Pratt); Boosting Interval-Based Literal: Variable Length and Early Classification (J J Rodriguez Diez); Segmenting Time Series: A Survey and Novel Approach (E Keogh et al.); Indexing Similar Time Series under Conditions of Noise (M Vlachos et al.); Classification of Events in Time Series of Graphs (H Bunke & M Kraetzl); Median Strings--A Review (X Jiang et al.); Change Detection in Classfication Models of Data Mining (G Zeira et al.). Readership: Graduate students, reseachers and practitioners in the fields of data mining, machine learning, databases and statistics.

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Non-Fiction Computing & I.T.Databases Data Mining Mathematics Probability & Statistics Computer Science Artificial Intelligence Booktopia Publisher Services World Scientific Publishing

Segmenting time series : a survey and novel approach	p. 1
A survey of recent methods for efficient retrieval of similar time sequences	p. 23
Indexing of compressed time series	p. 43
Indexing time-series under conditions of noise	p. 65
Change detection in classification models induced from time series data	p. 99
Classification and detection of abnormal events in time series of graphs	p. 123
Boosting interval-based literals : variable length and early classification	p. 145
Median strings : a review	p. 167
Table of Contents provided by Blackwell. All Rights Reserved.

Data Mining In Time Series Databases

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Hardcover

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