Search results
Results from the WOW.Com Content Network
MALLET is an integrated collection of Java code useful for statistical natural language processing, document classification, cluster analysis, information extraction, topic modeling and other machine learning applications to text.
Different methods for correlation clustering of this type are discussed in [12] and the relationship to different types of clustering is discussed in. [13] See also Clustering high-dimensional data. Correlation clustering (according to this definition) can be shown to be closely related to biclustering. As in biclustering, the goal is to ...
The Java just-in-time compiler optimizes all combinations to a similar extent, making benchmarking results more comparable if they share large parts of the code. When developing new algorithms or index structures, the existing components can be easily reused, and the type safety of Java detects many programming errors at compile time.
Second, it is conceptually close to nearest neighbor classification, and as such is popular in machine learning. Third, it can be seen as a variation of model-based clustering, and Lloyd's algorithm as a variation of the Expectation-maximization algorithm for this model discussed below. k-means clustering examples
Environment for DeveLoping KDD-Applications Supported by Index-Structures is a similar project to Weka with a focus on cluster analysis, i.e., unsupervised methods. H2O.ai is an open-source data science and machine learning platform; KNIME is a machine learning and data mining software implemented in Java.
Automatic clustering algorithms are algorithms that can perform clustering without prior knowledge of data sets. In contrast with other cluster analysis techniques, automatic clustering algorithms can determine the optimal number of clusters even in the presence of noise and outlier points. [1] [needs context]
DBSCAN optimizes the following loss function: [10] For any possible clustering = {, …,} out of the set of all clusterings , it minimizes the number of clusters under the condition that every pair of points in a cluster is density-reachable, which corresponds to the original two properties "maximality" and "connectivity" of a cluster: [1]
In statistics, canonical-correlation analysis (CCA), also called canonical variates analysis, is a way of inferring information from cross-covariance matrices.If we have two vectors X = (X 1, ..., X n) and Y = (Y 1, ..., Y m) of random variables, and there are correlations among the variables, then canonical-correlation analysis will find linear combinations of X and Y that have a maximum ...