Numerical
... 1.2 Bisection Method Let us suppose we have an equation of the form f(x) = 0 in which solution lies between in the range (a,b). Also f(x) is continuous and it can be algebraic or transcendental. If f(a) and f(b) are opposite signs, then there exist atleast one real root between a and b. Let f(a) be ...
... 1.2 Bisection Method Let us suppose we have an equation of the form f(x) = 0 in which solution lies between in the range (a,b). Also f(x) is continuous and it can be algebraic or transcendental. If f(a) and f(b) are opposite signs, then there exist atleast one real root between a and b. Let f(a) be ...
Aalborg Universitet Co-clustering for Weblogs in Semantic Space
... a result, a simultaneous grouping of both sets of users and pages is more appropriate and meaningful in modeling user navigational behavior and adapting the web design, in some contexts [5–7]. Although a considerable amount of researches of co-clustering on weblogs have been done, the major approach ...
... a result, a simultaneous grouping of both sets of users and pages is more appropriate and meaningful in modeling user navigational behavior and adapting the web design, in some contexts [5–7]. Although a considerable amount of researches of co-clustering on weblogs have been done, the major approach ...
Document
... • m-2k times: each occurrence of x in A that is not paired by another x (there are k pairs xx in A, thus m-2k of other occurrences of x in A) is paired by some other value (different than x) which gives at least 2k+(m-2k) = m occurrences in total, and contradicts the fact that x is a majority in A. ...
... • m-2k times: each occurrence of x in A that is not paired by another x (there are k pairs xx in A, thus m-2k of other occurrences of x in A) is paired by some other value (different than x) which gives at least 2k+(m-2k) = m occurrences in total, and contradicts the fact that x is a majority in A. ...
"Efficient Kernel Clustering using Random Fourier Features"
... Clustering, a primary data mining task, has found use in a variety of applications such as web search, social network analysis, image retrieval, medical imaging, gene expression analysis, recommendation systems and market analysis [1]. Kernel-based clustering techniques, which employ a nonlinear dis ...
... Clustering, a primary data mining task, has found use in a variety of applications such as web search, social network analysis, image retrieval, medical imaging, gene expression analysis, recommendation systems and market analysis [1]. Kernel-based clustering techniques, which employ a nonlinear dis ...
YADING: Fast Clustering of Large-Scale Time Series Data
... data into categorical attributes, thus making it possible to analyze time series data together with other categorical attributes. For example, if a group of servers have similar network usage profiles that consist of unusual spikes, and they also share the same router, then it is reasonable for the ...
... data into categorical attributes, thus making it possible to analyze time series data together with other categorical attributes. For example, if a group of servers have similar network usage profiles that consist of unusual spikes, and they also share the same router, then it is reasonable for the ...
Sampling Large Databases for Association Rules
... in the analyzed relation r. For instance, assume that the rule {beer} + {chips} has a confidence of 87 %. This means that if a randomly chosen basket contains beer, then it also contains chips with probability 0.87. A confidence threshold is used to exclude rules that are not strong enough to be int ...
... in the analyzed relation r. For instance, assume that the rule {beer} + {chips} has a confidence of 87 %. This means that if a randomly chosen basket contains beer, then it also contains chips with probability 0.87. A confidence threshold is used to exclude rules that are not strong enough to be int ...
Construction of Deterministic, Consistent, and Stable Explanations from Numerical Data and Prior Domain Knowledge
... 10. The APRIORI-C algorithm [34], which is based on the APRIORI algorithm [3, 4] encodes missing values by representing them as another numerical case. In principle, this may lead to a conclusion when most or all values are actually missing. In medicine, this would be unacceptable. The algorithm has ...
... 10. The APRIORI-C algorithm [34], which is based on the APRIORI algorithm [3, 4] encodes missing values by representing them as another numerical case. In principle, this may lead to a conclusion when most or all values are actually missing. In medicine, this would be unacceptable. The algorithm has ...
Statistical Learning Theory
... value, that is it should choose f (x) = 1. Now assume that the classes slightly overlap, for example P (Y = 1 X = x) = 0.9. This still means that in an overwhelming number of cases (in 90 % of them), the label of object x is +1, thus this is what the classifier f should choose. The same holds as ...
... value, that is it should choose f (x) = 1. Now assume that the classes slightly overlap, for example P (Y = 1 X = x) = 0.9. This still means that in an overwhelming number of cases (in 90 % of them), the label of object x is +1, thus this is what the classifier f should choose. The same holds as ...
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.