tailieunhanh - Data Mining and Knowledge Discovery Handbook, 2 Edition part 110

Data Mining and Knowledge Discovery Handbook, 2 Edition part 110. 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. | 1070 Chotirat Ann Ratanamahatana et al. Fig. . A visualization of the PAA dimensionality reduction technique mean value of all the data points in segment and the second number records the length of the segment. It is difficult to make any intuitive guess about the relative performance of this technique. On one hand PAA has the advantage of having twice as many approximating segments. On the other hand APCA has the advantage of being able to place a single segment in an area of low activity and many segments in areas of high activity. In addition one has to consider the structure of the data in question. It is possible to construct artificial datasets where one approach has an arbitrarily large reconstruction error while the other approach has reconstruction error of zero. Fig. . A visualization of the APCA dimensionality reduction technique In general finding the optimal piecewise polynomial representation of a time series requires a O Nn2 dynamic programming algorithm Faloutsos et al. 1997 . For most purposed however an optimal representation is not required. Most researchers therefore use a greedy suboptimal approach instead Keogh and Smyth 1997 . In Keogh etal. 2001 the authors utilize an original algorithm which produces high quality approximations in O nlog n . The algorithm works by first converting the problem into a wavelet compression problem for which there are well-known optimal solutions then converting the solution back to the APCA representation and possible making minor modification. 56 Mining Time Series Data 1071 Symbolic Aggregate Approximation SAX Symbolic Aggregate Approximation is a novel symbolic representation for time series recently introduced by Lin et al. 2003 which has been shown to preserve meaningful information from the original data and produce competitive results for classifying and clustering time series. The basic idea of SAX is to convert the data into a discrete format with a small alphabet size. In this case .

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