A Fast Algorithm For Data Mining - SJSU ScholarWorks
... 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 ...
![Density Based Clustering - DBSCAN [Modo de Compatibilidade]](http://s1.studyres.com/store/data/002454047_1-be20c7200358e2e97b22d406311261a6-300x300.png)
Predicting Customer Value
... Hazard models are used to estimate the shape of the hazard function (the time effect) and how the shape is affected by the covariates. Predictive hazard models can be used to score customers. The inputs are the current customer tenure and the current values of the other covariates. The output is the ...
... Hazard models are used to estimate the shape of the hazard function (the time effect) and how the shape is affected by the covariates. Predictive hazard models can be used to score customers. The inputs are the current customer tenure and the current values of the other covariates. The output is the ...
Learning Dissimilarities for Categorical Symbols
... To compare our Learned Dissimilarity approach, with those learned from other ten methods mentions in Section 2, we evaluate the classification accuracy of the nearest neighbor classifier, where the distances are computed from various dissimilarity measures. More specifically, the distance between tw ...
... To compare our Learned Dissimilarity approach, with those learned from other ten methods mentions in Section 2, we evaluate the classification accuracy of the nearest neighbor classifier, where the distances are computed from various dissimilarity measures. More specifically, the distance between tw ...
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.