Vignette
... cross validation. Note that TIGER is also tuning insensitive. Therefore we manually choose the p p regularization to be log(d)/n = log(400)/200 (This is also a theoretically consistent choice). We then compare the obtained graphs with the true graph using the visualization function in our package, a ...
... cross validation. Note that TIGER is also tuning insensitive. Therefore we manually choose the p p regularization to be log(d)/n = log(400)/200 (This is also a theoretically consistent choice). We then compare the obtained graphs with the true graph using the visualization function in our package, a ...
3. Answering Cube Queries Using Statistics Trees
... dj+1, where j = 1,2,...,k. The first dj pointers point to the subtrees which store information for the jth column value of the input data. The (dj+1)th pointer is called star pointer which leads to a region in the tree where this domain has been “collapsed,” meaning it contains all of the domain val ...
... dj+1, where j = 1,2,...,k. The first dj pointers point to the subtrees which store information for the jth column value of the input data. The (dj+1)th pointer is called star pointer which leads to a region in the tree where this domain has been “collapsed,” meaning it contains all of the domain val ...
Density Connected Clustering with Local Subspace Preferences
... cluster and S is a set of attributes spanning the subspace in which C exists. Mapping each cluster to an associated subspace allows more flexibility than global methods projecting the entire data set onto a single subspace. In the example given in Figure 1, a subspace clustering algorithm will find th ...
... cluster and S is a set of attributes spanning the subspace in which C exists. Mapping each cluster to an associated subspace allows more flexibility than global methods projecting the entire data set onto a single subspace. In the example given in Figure 1, a subspace clustering algorithm will find th ...
AN EFFICIENT HILBERT CURVE
... Recently, millions of databases have been used and we need a new technique that can automatically transform the processed data into useful information and knowledge. Data mining is the technique of analyzing data to discover previously unknown information and spatial data mining is the branch of dat ...
... Recently, millions of databases have been used and we need a new technique that can automatically transform the processed data into useful information and knowledge. Data mining is the technique of analyzing data to discover previously unknown information and spatial data mining is the branch of dat ...
Simple Regression
... Standard error measures how much the points vary about the regression line. Roughly, it’s a measure of how close we could expect an actual Y to be to its predicted Y. A large Standard Error of Estimate means that prediction is poor. A small Standard Error of Estimate means that the prediction equati ...
... Standard error measures how much the points vary about the regression line. Roughly, it’s a measure of how close we could expect an actual Y to be to its predicted Y. A large Standard Error of Estimate means that prediction is poor. A small Standard Error of Estimate means that the prediction equati ...
TM-LDA: efficient online modeling of latent topic transitions in social
... Notice that both D(1,m) and D(2,m+1) are m × n matrices. The i-th rows of these two matrices are di and di+1 , and they are sequentially adjacent in the collection D. In other words, D(1,m) represents the topic distribution matrix of “old” documents and D(2,m+1) is the matrix of “new” documents. Acc ...
... Notice that both D(1,m) and D(2,m+1) are m × n matrices. The i-th rows of these two matrices are di and di+1 , and they are sequentially adjacent in the collection D. In other words, D(1,m) represents the topic distribution matrix of “old” documents and D(2,m+1) is the matrix of “new” documents. Acc ...
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.