Subgroup Discovery with CN2-SD - Bristol CS
... Subgroup discovery is usually seen as different from classification, as it addresses different goals (discovery of interesting population subgroups instead of maximizing classification accuracy of the induced rule set). This is manifested also by the fact that in subgroup discovery one can often tol ...
... Subgroup discovery is usually seen as different from classification, as it addresses different goals (discovery of interesting population subgroups instead of maximizing classification accuracy of the induced rule set). This is manifested also by the fact that in subgroup discovery one can often tol ...
Research Proposal - University of South Australia
... This algorithm was defined by Charu C. Aggarwal and Philip S. Yu in 2001 (Aggarwal et al. 2001). Charu Aggarwal has written extensively on the topic of data mining under high dimensionality since the year 2000. This algorithm is the earliest subspace outlier detection algorithm the author of this pr ...
... This algorithm was defined by Charu C. Aggarwal and Philip S. Yu in 2001 (Aggarwal et al. 2001). Charu Aggarwal has written extensively on the topic of data mining under high dimensionality since the year 2000. This algorithm is the earliest subspace outlier detection algorithm the author of this pr ...
Exploiting A Support-based Upper Bound of Pearson`s Correlation
... corrElation queRy (TAPER) algorithm. The TAPER algorithm is a two-step filter-and-refine query processing strategy which consists of two steps: filtering and refinement. The Filtering Step: In this step, the TAPER algorithm applies two pruning techniques. The first technique uses the upper bound of ...
... corrElation queRy (TAPER) algorithm. The TAPER algorithm is a two-step filter-and-refine query processing strategy which consists of two steps: filtering and refinement. The Filtering Step: In this step, the TAPER algorithm applies two pruning techniques. The first technique uses the upper bound of ...
A Class Imbalance Learning Approach to Fraud
... which attracts many users in many ages. Most of these games offer free versions from which companies make money by showing small advertisements in those game screens. As such mobile advertisements become increasingly popular and a crucial method of attracting customers. Due to the limitations of mob ...
... which attracts many users in many ages. Most of these games offer free versions from which companies make money by showing small advertisements in those game screens. As such mobile advertisements become increasingly popular and a crucial method of attracting customers. Due to the limitations of mob ...
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.