For Review Only - Universidad de Granada

... – In clustering [Har75], the process consists of splitting the data into several groups, with the examples belonging to each group being as similar as possible among them. – Association [AIS93] is devoted to identify relation between transactional data. • Semi-supervised learning: This type of probl ...

... – In clustering [Har75], the process consists of splitting the data into several groups, with the examples belonging to each group being as similar as possible among them. – Association [AIS93] is devoted to identify relation between transactional data. • Semi-supervised learning: This type of probl ...

Statistical Methods (201112)

... be internally consistent. In general, Statistics Netherlands can provide better imputations for general use than external users, because these parties often do not have all of the background characteristics that are useful for the imputation. 1.1.2 Problem and solutions 1.1.2.1 Reading guide Sometim ...

... be internally consistent. In general, Statistics Netherlands can provide better imputations for general use than external users, because these parties often do not have all of the background characteristics that are useful for the imputation. 1.1.2 Problem and solutions 1.1.2.1 Reading guide Sometim ...

A Survey of Online Failure Prediction Methods

... In summary, accurate online failure prediction is only the prerequisite in the chain and each of the remaining three steps constitutes a whole field of research on its own. Not devaluing the efforts that have been made in the other fields, this survey provides an overview of online failure predictio ...

... In summary, accurate online failure prediction is only the prerequisite in the chain and each of the remaining three steps constitutes a whole field of research on its own. Not devaluing the efforts that have been made in the other fields, this survey provides an overview of online failure predictio ...

TOWARD ACCURATE AND EFFICIENT OUTLIER DETECTION IN

... Because of that, outlier detection has several important applications. Outliers can provide interesting insight about the dataset. For example, the network activities that is surprisingly high with respect to its network may indicate an error or a network attack in the system. The appearance of an o ...

... Because of that, outlier detection has several important applications. Outliers can provide interesting insight about the dataset. For example, the network activities that is surprisingly high with respect to its network may indicate an error or a network attack in the system. The appearance of an o ...

as a PDF

... algorithms for clustering large scale transactional datasets. A transactional dataset consists of N transactions, each of which contains varying number of items. For example, t1 = {milk, bread, beer} and t2 = {milk, bread} are three-item transaction and two-item transaction respectively. A transacti ...

... algorithms for clustering large scale transactional datasets. A transactional dataset consists of N transactions, each of which contains varying number of items. For example, t1 = {milk, bread, beer} and t2 = {milk, bread} are three-item transaction and two-item transaction respectively. A transacti ...

# 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.