A Fast Algorithm For Data Mining
... To address the limitations of Apriori for mining long patterns, alternate approaches have been considered in the literature [Lin2002, Lin2003]. One approach is to mine the database for closed frequent itemsets. A frequent itemset N is said to be closed if and only if there does not exist another fr ...
... To address the limitations of Apriori for mining long patterns, alternate approaches have been considered in the literature [Lin2002, Lin2003]. One approach is to mine the database for closed frequent itemsets. A frequent itemset N is said to be closed if and only if there does not exist another fr ...
15-388/688 - Practical Data Science: Unsupervised learning
... The k-means objective is non-convex (possibility of local optima), and does not have a closed form solution, so we resort to an approximate method, by repeating the following (Lloyd’s algorithm, or just “k-means”) 1. Assign points to nearest cluster 2. Compute cluster center as mean of all points as ...
... The k-means objective is non-convex (possibility of local optima), and does not have a closed form solution, so we resort to an approximate method, by repeating the following (Lloyd’s algorithm, or just “k-means”) 1. Assign points to nearest cluster 2. Compute cluster center as mean of all points as ...
Methods for Uncertainty and Sensitivity Analysis. Review and recommendations for
... sources. For instance the value of the sorption coefficient of nuclide is not known exactly; in fact only a given range or perhaps some “best” estimators may be specified with good certainty. We say that this parameter has an uncertainty and this uncertainty at the input of the model will propagate ...
... sources. For instance the value of the sorption coefficient of nuclide is not known exactly; in fact only a given range or perhaps some “best” estimators may be specified with good certainty. We say that this parameter has an uncertainty and this uncertainty at the input of the model will propagate ...
Near-Optimal Algorithms for Maximum Constraint Satisfaction Problems Moses Charikar Konstantin Makarychev
... (where c > 0.44 is an absolute constant). This result improves the previously best known algorithm by Hast, which had an approximation guarantee of Ω(k/(2k log k)). Both results are optimal assuming the Unique Games Conjecture and are based on rounding natural semidefinite programming relaxations. W ...
... (where c > 0.44 is an absolute constant). This result improves the previously best known algorithm by Hast, which had an approximation guarantee of Ω(k/(2k log k)). Both results are optimal assuming the Unique Games Conjecture and are based on rounding natural semidefinite programming relaxations. W ...
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.