数据挖掘双语教学课件10appl幻灯片.pptVIP

* Data Mining: Concepts and Techniques * Scientific and Statistical Data Mining (1) There are many well-established statistical techniques for data analysis, particularly for numeric data applied extensively to data from scientific experiments and data from economics and the social sciences Regression predict the value of a response (dependent) variable from one or more predictor (independent) variables where the variables are numeric forms of regression: linear, multiple, weighted, polynomial, nonparametric, and robust Generalized linear models allow a categorical response variable (or some transformation of it) to be related to a set of predictor variables similar to the modeling of a numeric response variable using linear regression include logistic regression and Poisson regression * Data Mining: Concepts and Techniques * Scientific and Statistical Data Mining (2) Regression trees Binary trees used for classification and prediction Similar to decision trees:Tests are performed at the internal nodes Difference is at the leaf level In a decision tree a majority voting is performed to assign a class label to the leaf In a regression tree the mean of the objective attribute is computed and used as the predicted value Analysis of variance Analyze experimental data for two or more populations described by a numeric response variable and one or more categorical variables (factors) Mixed-effect models For analyzing grouped data, i.e. data that can be classified according to one or more grouping variables Typically describe relationships between a response variable and some covariates in data grouped according to one or more factors * Data Mining: Concepts and Techniques * Scientific and Statistical Data Mining (3) Factor analysis determine which vars are combined to generate a given factor e.g., for many psychiatric data, one can indirectly measure other quantities (such as test scores) that reflect the factor of interest Discriminant analysis predict a categorical