Data Mining In Time Series Databases (Series in Machine Perception and Artificial Intelligence)

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. 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.--jacket. Segmenting Time Series : A Survey And Novel Approach / E. Keogh ... [et Al.] -- A Survey Of Recent Methods For Efficient Retrieval Of Similar Time Sequences / M.l. Hetland -- Indexing Of Compressed Time Series / E. Fink And K.b. Pratt -- Indexing Time-series Under Conditions Of Noise / M. Vlachos, D. Gunopulos, And G. Das -- Change Detection In Classification Models Induced From Time Series Data / G. Zeira ... [et Al.] -- Classification And Detection Of Abnormal Events In Time Series Of Graphs / H. Bunke And M. Kraetzl -- Boosting Interval-based Literals : Variable Length And Early Classification / C.j. Alonso González And J.j. Rodríguez Diez -- Median Strings : A Review / X. Jiang, H. Bunke, And J. Csirik. Editors, Mark Last, Abraham Kandel, Horst Bunke. Includes Bibliographical References. 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 manual examines 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 text 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 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 This thin book presents eight academic papers discussing handling of sequences. I did not find any of them interesting on its own or good as a survey, but academics doing research in machine learning may disagree. If you are one, you most likely can get the original papers. If you are a practitioner, pass without a second thought. In recent years, there has been an explosion of interest in mining time series databases.