Finding Association Rules From Quantitative Data Using Data Booleanization
... Boolean Analyzer attaches to each discovered rule a PIM value. The PIM imposes an order or ranking on the set of rules. Although we were able to show that the known rules were found easily by using mean, median, and expert-defined thresholds, the question remained, “Was the order imposed on the rule ...
... Boolean Analyzer attaches to each discovered rule a PIM value. The PIM imposes an order or ranking on the set of rules. Although we were able to show that the known rules were found easily by using mean, median, and expert-defined thresholds, the question remained, “Was the order imposed on the rule ...
Minimum Entropy Clustering and Applications to Gene Expression
... measures are based is similar to that of probabilistic dependence. Thus, we use entropy measured on a posteriori probabilities as the criterion for clustering. In fact, it is the conditional entropy of clusters given the observations. Thus, Fano’s inequality indicates that the minimum entropy may be ...
... measures are based is similar to that of probabilistic dependence. Thus, we use entropy measured on a posteriori probabilities as the criterion for clustering. In fact, it is the conditional entropy of clusters given the observations. Thus, Fano’s inequality indicates that the minimum entropy may be ...
Clustering methods for Big data analysis
... Today the massive data explosion is the result of a dramatic increase in the devices located at the periphery of the network including embedded sensors, smart phones and tablet computers. These large volumes of data sets are produced by the employees of the companies, social networking sites and dif ...
... Today the massive data explosion is the result of a dramatic increase in the devices located at the periphery of the network including embedded sensors, smart phones and tablet computers. These large volumes of data sets are produced by the employees of the companies, social networking sites and dif ...
Semiparametric regression analysis with missing response at ramdom
... We propose several estimators of θ in the partially linear model that are simple to compute and do not rely on high dimensional smoothing, thereby avoiding the curse of dimensionality. Our class of estimators includes an imputation estimator and a number of propensity score weighting estimators. Und ...
... We propose several estimators of θ in the partially linear model that are simple to compute and do not rely on high dimensional smoothing, thereby avoiding the curse of dimensionality. Our class of estimators includes an imputation estimator and a number of propensity score weighting estimators. Und ...
Expectation–maximization algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.