Spatial Outlier Detection Approaches and Methods: A Survey
... density-connected if there is a point o such that both p and q are density-reachable from o. DBSCAN requires two parameters, the threshold value (eps) and the minimum number of points required to form a cluster (minPts). It starts with an arbitrary starting point. From this point the -neighborhood i ...
... density-connected if there is a point o such that both p and q are density-reachable from o. DBSCAN requires two parameters, the threshold value (eps) and the minimum number of points required to form a cluster (minPts). It starts with an arbitrary starting point. From this point the -neighborhood i ...
SPRITE: A Data-Driven Response Model For Multiple Choice Questions
... response model (NRM) [6], produce student parameters that are not necessarily correlated with their abilities [30], which is critical to many applications where student ability estimates are needed to provide feedback on their strengths and weaknesses. The second type of model treats the options as ...
... response model (NRM) [6], produce student parameters that are not necessarily correlated with their abilities [30], which is critical to many applications where student ability estimates are needed to provide feedback on their strengths and weaknesses. The second type of model treats the options as ...
PARTCAT: A Subspace Clustering Algorithm for High Dimensional Categorical Data
... algorithm may not differentiate two dissimilar data points and may produce a single cluster containing all data points. The algorithm PARTCAT proposed in this paper follows the same neural architecture as PART. The principal difference between PARTCAT and PART is the up-bottom weight and the learnin ...
... algorithm may not differentiate two dissimilar data points and may produce a single cluster containing all data points. The algorithm PARTCAT proposed in this paper follows the same neural architecture as PART. The principal difference between PARTCAT and PART is the up-bottom weight and the learnin ...
Dirichlet Process Mixtures of Generalized Linear
... This means that each mi (x) is a linear function, but a non-linear mean function arises when we marginalize out the uncertainty about which local response distribution is in play. (See Figure 1 for an example with one covariate and a continuous response function.) Furthermore, our method captures he ...
... This means that each mi (x) is a linear function, but a non-linear mean function arises when we marginalize out the uncertainty about which local response distribution is in play. (See Figure 1 for an example with one covariate and a continuous response function.) Furthermore, our method captures he ...
BC26354358
... substantially reduced the search space by pruning candidate item sets using the lower bounding distance of the bounds of support sequences, and the monotonicity property of the upper lower-bounding ...
... substantially reduced the search space by pruning candidate item sets using the lower bounding distance of the bounds of support sequences, and the monotonicity property of the upper lower-bounding ...
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.