Data Mining Techniques for Informative Motif Discovery
... This method reduces the required computational time to extract frequent sequence patterns. They provide a user interface that permits a database to be queried in different ways. The modified prefix span has been applied to the evaluation of three set of sequences that include the Zinc Finger, Cytoch ...
... This method reduces the required computational time to extract frequent sequence patterns. They provide a user interface that permits a database to be queried in different ways. The modified prefix span has been applied to the evaluation of three set of sequences that include the Zinc Finger, Cytoch ...
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... error rate that is less than 30% is indicative of the inherent overlap that exists in the two samples and not necessarily a failure of all the methods. In other words, high misclassification error rates may be unavoidable for this particular sample regardless of the procedure used. This paper propos ...
... error rate that is less than 30% is indicative of the inherent overlap that exists in the two samples and not necessarily a failure of all the methods. In other words, high misclassification error rates may be unavoidable for this particular sample regardless of the procedure used. This paper propos ...
CHAPTER-17 Decision Tree Induction 17.1 Introduction 17.2
... The naïve Bayesian classifier makes the assumption of class conditional independence, that is , given the class label of a sample, the values of the attributes are conditionally independent of the other. This assumption simplifies computation. When the assumption holds true, then the naïve Bayesian ...
... The naïve Bayesian classifier makes the assumption of class conditional independence, that is , given the class label of a sample, the values of the attributes are conditionally independent of the other. This assumption simplifies computation. When the assumption holds true, then the naïve Bayesian ...
Data Mining
... • Strength: Relatively efficient: O(tkn), where n is # objects, k is # clusters, and t is # iterations. Normally, k, t << n. • Comparing: PAM: O(k(n-k)2 ), CLARA: O(ks2 + k(n-k)) • Comment: Often terminates at a local optimum. The global optimum may be found using techniques such as: deterministic a ...
... • Strength: Relatively efficient: O(tkn), where n is # objects, k is # clusters, and t is # iterations. Normally, k, t << n. • Comparing: PAM: O(k(n-k)2 ), CLARA: O(ks2 + k(n-k)) • Comment: Often terminates at a local optimum. The global optimum may be found using techniques such as: deterministic a ...
Regression
... of the demand curve overestimated the true slope by 10 units per £? That’s important ...
... of the demand curve overestimated the true slope by 10 units per £? That’s important ...
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.