Data Mining In Time Series Databases

Horst Bunke, Abraham Kandel, Mark Last

• Publisher: World Scientific
• Year: 2004
• Print ISBN: 9789812382900, 9789812382900
• eBook ISBN: 9789812565402
• Topic: Computer Science
• Subtopic: Data Mining
• Index: Computer Science

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.

Table of contents

• Contents
• Preface
• Chapter 1 Segmenting Time Series: A Survey and Novel Approach E. Keogh, S. Chu, D. Hart and M. Pazzani
• Chapter 2 A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences M. L. Hetland
• Chapter 3 Indexing of Compressed Time Series E. Fink and K. B. Pratt
• Chapter 4 Indexing Time-Series under Conditions of Noise M. Vlachos, D. Gunopulos and G. Das
• Chapter 5 Change Detection in Classification Models Induced from Time Series Data G. Zeira, O. Maimon, M. Last and L. Rokach
• Chapter 6 Classification and Detection of Abnormal Events in Time Series of Graphs H. Bunke and M. Kraetzl
• Chapter 7 Boosting Interval-Based Literals: Variable Length and Early Classification C. J. Alonso Gonzalez and J. J. Rodriguez Diez
• Chapter 8 Median Strings: A Review X. Jiang, H. Bunke and J. Csirik