数据挖掘双语教学课件3prep幻灯片.pptVIP

* Data Mining: Concepts and Techniques * X1 X2 Y1 Y2 Principal Component Analysis * Data Mining: Concepts and Techniques * Numerosity Reduction Parametric methods Assume the data fits some model, estimate model parameters, store only the parameters, and discard the data (except possible outliers) Log-linear models: obtain value at a point in m-D space as the product on appropriate marginal subspaces Non-parametric methods Do not assume models Major families: histograms, clustering, sampling * Data Mining: Concepts and Techniques * Regression and Log-Linear Models Linear regression: Data are modeled to fit a straight line Often uses the least-square method to fit the line Multiple regression: allows a response variable Y to be modeled as a linear function of multidimensional feature vector Log-linear model: approximates discrete multidimensional probability distributions Linear regression: Y = ? + ? X Two parameters , ? and ? specify the line and are to be estimated by using the data at hand. using the least squares criterion to the known values of Y1, Y2, …, X1, X2, …. Multiple regression: Y = b0 + b1 X1 + b2 X2. Many nonlinear functions can be transformed into the above. Log-linear models: The multi-way table of joint probabilities is approximated by a product of lower-order tables. Probability: p(a, b, c, d) = ?ab ?ac?ad ?bcd Regress Analysis and Log-Linear Models * Data Mining: Concepts and Techniques * Histograms A popular data reduction technique Divide data into buckets and store average (sum) for each bucket Can be constructed optimally in one dimension using dynamic programming Related to quantization problems. * Data Mining: Concepts and Techniques * Clustering Partition data set into clusters, and one can store cluster representation only Can be very effective if data is clustered but not if data is “smeared” Can have hierarchical clustering and be stored in multi-dimensional index tree structures There are many choices of clustering definitions and cl