Data Mining in Time Series Databases
by Mark LastThis 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… See more details below
Overview
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.
Product Details
- ISBN-13:
- 9789812382900
- Publisher:
- World Scientific Publishing Company, Incorporated
- Publication date:
- 11/01/2004
- Series:
- Series in Machine Perception and Artificial Intelligence
- Pages:
- 204
- Product dimensions:
- 6.00(w) x 9.00(h) x 0.80(d)
Table of Contents
| Ch. 1 | Segmenting time series : a survey and novel approach | 1 |
| Ch. 2 | A survey of recent methods for efficient retrieval of similar time sequences | 23 |
| Ch. 3 | Indexing of compressed time series | 43 |
| Ch. 4 | Indexing time-series under conditions of noise | 65 |
| Ch. 5 | Change detection in classification models induced from time series data | 99 |
| Ch. 6 | Classification and detection of abnormal events in time series of graphs | 123 |
| Ch. 7 | Boosting interval-based literals : variable length and early classification | 145 |
| Ch. 8 | Median strings : a review | 167 |
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