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... uncertainty about the unknown parameter Uses probability to quantify this uncertainty: Unknown parameters as random variables Prediction follows from the rules of probability: Expectation over the unknown parameters ...
... uncertainty about the unknown parameter Uses probability to quantify this uncertainty: Unknown parameters as random variables Prediction follows from the rules of probability: Expectation over the unknown parameters ...
Iterative Discovery of Multiple Alternative Clustering Views
... can find multiple alternative views by clustering in the subspace orthogonal to the clustering solutions found in previous iterations. They directly address the problem of finding several (more than two) alternative clustering solutions by iteratively finding one alternative solution given the previ ...
... can find multiple alternative views by clustering in the subspace orthogonal to the clustering solutions found in previous iterations. They directly address the problem of finding several (more than two) alternative clustering solutions by iteratively finding one alternative solution given the previ ...
Kunling Zeng Review of the Literature Outline EAP 508 P02 11/9
... Hundreds of literature have been proposed to improve the traditional K-Means [1,2,3,4,5,6,13,14,15]. Although K-Means is very widely studied and used, it does suffer some disadvantages such as it is very sensitive to initialization [12], it converges to local optimum [11], does not offer quality gua ...
... Hundreds of literature have been proposed to improve the traditional K-Means [1,2,3,4,5,6,13,14,15]. Although K-Means is very widely studied and used, it does suffer some disadvantages such as it is very sensitive to initialization [12], it converges to local optimum [11], does not offer quality gua ...
efficient algorithms for mining arbitrary shaped clusters
... would not have been anywhere close, had it not been for the following people. First and foremost, I sincerely thank my adviser, Professor Zaki for his support, both in research and otherwise. He has this amazing style of advising – giving freedom but at the same time questioning; helping us to think ...
... would not have been anywhere close, had it not been for the following people. First and foremost, I sincerely thank my adviser, Professor Zaki for his support, both in research and otherwise. He has this amazing style of advising – giving freedom but at the same time questioning; helping us to think ...
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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.