Data Mining In Time Series Databases

✍ Scribed by Mark Last, Abraham Kandel, Horst Bunke

• Publisher: World Scientific
• Year: 2004
• Tongue: English
• Leaves: 205
• Series: Series in machine perception and artificial intelligence v.57
• Category: Library

✦ Synopsis

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

Team-kb......Page 1
Contents......Page 12
Segmenting Time Series: A Survey And Novel Approach......Page 14
A Survey Of Recent Methods For Efficient Retrieval Of Similar Time Sequences......Page 36
Indexing Of Compressed Time Series......Page 56
Indexing Time-series Under Conditions Of Noise......Page 80
Change Detection In Classification Models Induced From Time Series Data......Page 114
Classification And Detection Of Abnormal Events In Time Series Of Graphs......Page 140
Þÿ......Page 162
Median Strings: A Review......Page 186

✦ Subjects

Информатика и вычислительная техника;Искусственный интеллект;Интеллектуальный анализ данных;