tailieunhanh - Data Mining and Knowledge Discovery Handbook, 2 Edition part 108

Data Mining and Knowledge Discovery Handbook, 2 Edition part 108. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 1050 Chotirat Ann Ratanamahatana et al. Indexing Query by Content Given a query time series Q and some similarity dissimilarity measure D Q C find the most similar time series in database DB Chakrabarti et al. 2002 Faloutsos etal. 1994 Kahveci and Singh 2001 Popivanov et al. 2002 . Clustering Find natural groupings of the time series in database DB under some similarity dissimilarity measure D Q C Aach and Church 2001 Debregeas and Hebrail 1998 Kalpakis et al. 2001 Keogh and Pazzani 1998 . Classification Given an unlabeled time series Q assign it to one of two or more predefined classes Geurts 2001 Keogh and Pazzani 1998 . Prediction Forecasting Given a time series Q containing n data points predict the value at time n 1. Summarization Given a time series Q containing n data points where n is an extremely large number create a possibly graphic approximation of Q which retains its essential features but fits on a single page computer screen etc. Indyk et al. 2000 Wijk and Selow 1999 . Anomaly Detection Interestingness Detection Given a time series Q assumed to be normal and an unannotated time series R find all sections of R which contain anomalies or surprising interesting unexpected occurrences Guralnik and Srivastava 1999 Keogh et al 2002 Shahabi et al 2000 . Segmentation a Given a time series Q containing n data points construct a model Q from K piecewise segments K n such that Q closely approximates Q Keogh and Pazzani 1998 . b Given a time series Q partition it into K internally homogenous sections also known as change detection Guralnik and Srivastava 1999 . Note that indexing and clustering make explicit use of a distance measure and many approaches to classification prediction association detection summarization and anomaly detection make implicit use of a distance measure. We will therefore take the time to consider time series similarity in detail. Time Series Similarity Measures Euclidean Distances and Lp Norms One of the simplest similarity