Data Mining and Knowledge Discovery Handbook (Springer series in solid-state sciences)
Lior Rokach
Language: English
Pages: 1285
ISBN: 0387098224
Format: PDF / Kindle (mobi) / ePub
This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.
High Fidelity Haptic Rendering (Synthesis Lectures on Computer Graphics and Animation)
Essentials of Error-Control Coding
Advanced Methods in Computer Graphics: With examples in OpenGL
Lior Rokach, Oded Maimon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 981 52 Information Fusion - Methods and Aggregation Operators Vicenc¸ Torra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 999 53 Parallel And Grid-Based Data Mining – Algorithms, Models and Systems for High-Performance KDD Antonio Congiusta, Domenico Talia, Paolo Trunfio . . . . . . . . . . . . . . . . . . . . . . 1009 Contents.
Obvious that PCA is eligible for the kernel trick, since in PCA the data appears in expectations over products of individual components of vectors, not over dot products between the vectors. However (Sch¨olkopf et al., 1998) show how the problem can indeed be cast entirely in terms of dot products. They make two key observations: first, that the eigenvectors of the covariance matrix in F lie in the span of the (centered) mapped data, and second, that therefore no information in the eigenvalue.
Approach. Technical Report FKI- 221- 97, Technische Universit at Munchen 1997. Setiono, R. and Liu, H. Chi2: Feature selection and discretization of numeric attributes. In Proceedings of the Seventh IEEE International Conference on Tools with Artificial Intelligence, 1995 Singh, M. and Provan, G. M. Efficient learning of selective Bayesian classifiers. In Machine Learning: Proceedings of the Thirteenth International network Conference on Machine Learning. Morgan Kaufmann, 1996. Skalak, B. Prototype.
(CUSUM) and Exponential Weighted Moving Average (EWMA) are model-specific for independent data. Note that these methods are extensively implemented in industry, although the independence assumptions are frequently violated in practice. 122 Irad Ben-Gal The majority of model-specific methods for dependent data are based on timeseries. Often, the underlying principle of these methods is as follows: find a time series model that can best capture the autocorrelation process, use this model to filter.
Conference and Data Warehousing and Knowledge Discovery (DaWaK02), Aix en Provence, France, 2002. Haining R., Spatial Data Analysis in the Social and Environmental Sciences. Cambridge University Press, 1993. Hampel F. R., ”A general qualitative definition of robustness,” Annals of Mathematics Statistics, 42, 1887–1896, 1971. Hampel F. R., ”The influence curve and its role in robust estimation,” Journal of the American Statistical Association, 69, 382–393, 1974. Haslett J., Brandley R., Craig P.,.