IOSR Journal of Computer Engineering (IOSR-JCE)
... algorithm uses the apriori principle, which says that the item set I containing item set X is never large if item set X is not large or all the non empty subset of frequent item set must be frequent also. Based on this principle, the apriori algorithm generates a set of candidate item sets whose len ...
... algorithm uses the apriori principle, which says that the item set I containing item set X is never large if item set X is not large or all the non empty subset of frequent item set must be frequent also. Based on this principle, the apriori algorithm generates a set of candidate item sets whose len ...
this PDF file - Southeast Europe Journal of Soft Computing
... Confidence{X⇒Y}=Occurrence {Y} Association rules have been used in many areas, such as: Market Basket Analysis: MBA is one of the most typical / Occurrence {X} application areas of ARM. When a customer buys any 1 Bread, Butter, Peanut product, what other products s/he puts in the basket with some 2 ...
... Confidence{X⇒Y}=Occurrence {Y} Association rules have been used in many areas, such as: Market Basket Analysis: MBA is one of the most typical / Occurrence {X} application areas of ARM. When a customer buys any 1 Bread, Butter, Peanut product, what other products s/he puts in the basket with some 2 ...
S5.2b - United Nations Statistics Division
... › Note that you look for the “best” model to flash estimate the « worst » figure (the first estimation will always be revised) › To do that, you use “non homogeneous” data (mixed between first, second, third … releases) › It is always better to estimate several models – In case a X-variable is not a ...
... › Note that you look for the “best” model to flash estimate the « worst » figure (the first estimation will always be revised) › To do that, you use “non homogeneous” data (mixed between first, second, third … releases) › It is always better to estimate several models – In case a X-variable is not a ...
Advanced Risk Management – 10
... portfolio level. But, can this be done at a policy level? If so, how can we separate the good or the bad policies based on some measures (Y) and their characteristics (X)? Most of the matured insurance markets around the globe are attempting to do more precise segmentation of good vs. bad risks, so ...
... portfolio level. But, can this be done at a policy level? If so, how can we separate the good or the bad policies based on some measures (Y) and their characteristics (X)? Most of the matured insurance markets around the globe are attempting to do more precise segmentation of good vs. bad risks, so ...
Graph-based consensus clustering for class discovery from gene
... framework, known as GCC, to discover the classes of the samples in gene expression data. • GCC can successfully estimate the true number of classes for the datasets in ...
... framework, known as GCC, to discover the classes of the samples in gene expression data. • GCC can successfully estimate the true number of classes for the datasets in ...
... Analysis of a head trauma dataset was aided by the use of a new, binary-based data mining technique which finds dependency/association rules. With initial guidance from a domain user or domain expert, Boolean Analyzer (BA) is given one or more metrics to partition the entire data set. The weighted r ...
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.