Data Mining in Time Series Databases
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.
- ISBN 13 : 9812382909
- ISBN 10 : 9789812382900
- Judul : Data Mining in Time Series Databases
- Pengarang : Mark Last, Abraham Kandel, Horst Bunke,
- Kategori : Mathematics
- Penerbit : World Scientific
- Bahasa : en
- Tahun : 2004
- Halaman : 192
- Halaman : 192
- Google Book : http://books.google.com/books?id=zV1pDQAAQBAJ&dq=intitle:Data+Mining&hl=&source=gbs_api
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Ketersediaan :
This book covers the state-of-the-art methodology for mining time series databases.