Clustering
... It is insensitive to the order of records in input and does not presume some canonical data distribution It scales linearly with the size of input and has good scalability as the number of dimensions in the data increases Weakness The accuracy of the clustering result may be degraded at the ex ...
... It is insensitive to the order of records in input and does not presume some canonical data distribution It scales linearly with the size of input and has good scalability as the number of dimensions in the data increases Weakness The accuracy of the clustering result may be degraded at the ex ...
ANR: An algorithm to recommend initial cluster centers for k
... the final clusters detected by k-means which have minimum clustering error will be used as the initial centers. Likas et al. [2] proposed the global k-means algorithm which incrementally adds one cluster center at any iteration through a deterministic global search procedure consisting of executing ...
... the final clusters detected by k-means which have minimum clustering error will be used as the initial centers. Likas et al. [2] proposed the global k-means algorithm which incrementally adds one cluster center at any iteration through a deterministic global search procedure consisting of executing ...
Full-Text - International Journal of Computer Science Issues
... relevance of the term to the category it belongs to as compared with its relevance to other documents. It has been proved that it has a consistently better performance than other term weighting methods while other supervised term weighting methods based on information theory or statistical metric pe ...
... relevance of the term to the category it belongs to as compared with its relevance to other documents. It has been proved that it has a consistently better performance than other term weighting methods while other supervised term weighting methods based on information theory or statistical metric pe ...
Attribute Selection with a Multiobjective Genetic Algorithm
... Note that attribute selection, like many other data mining problems, involve the “simultaneous” optimization of more than one objective. However, such a simultaneous optimization is not always possible. The objectives to be optimized can be conflicting with one another, and they normally are non-com ...
... Note that attribute selection, like many other data mining problems, involve the “simultaneous” optimization of more than one objective. However, such a simultaneous optimization is not always possible. The objectives to be optimized can be conflicting with one another, and they normally are non-com ...
Multivariate discretization by recursive supervised
... this structural gain may be balanced by an information loss. The first experiment aims at evaluating how our method is affected by such a flaw. We consider the resulting partition as a basic predictive model : a new instance is classified according to a majority vote in the nearest group. We thus compar ...
... this structural gain may be balanced by an information loss. The first experiment aims at evaluating how our method is affected by such a flaw. We consider the resulting partition as a basic predictive model : a new instance is classified according to a majority vote in the nearest group. We thus compar ...
Risk of Bayesian Inference in Misspecified Models, and the Sandwich Covariance Matrix
... allow for a pseudo-true interpretation of all parameters and would not require a prior at all). It is shown that an appropriate sequence of scores of the integrated likelihood can be obtained by computing posterior averages of the partial score of the original model conditional on increasing subset ...
... allow for a pseudo-true interpretation of all parameters and would not require a prior at all). It is shown that an appropriate sequence of scores of the integrated likelihood can be obtained by computing posterior averages of the partial score of the original model conditional on increasing subset ...
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.