Java* API Reference for Intel® Data Analytics Acceleration Library 2019

Modules
Here is a list of all modules:
[detail level 12345678]
 Algorithms
 Analysis
 Association RulesContains classes for the association rules algorithm
 BACON Outlier DetectionContains classes for computing the multivariate outlier detection
 Cholesky DecompositionContains classes for computing Cholesky decomposition
 Correlation Distance MatrixContains classes for computing the correlation distance
 Correlation and Variance-Covariance MatricesContains classes for computing the correlation or variance-covariance matrix
 Cosine Distance MatrixContains classes for computing the cosine distance
 DistributionsContains classes for distributions
 EnginesContains classes for engines
 Expectation-MaximizationContains classes for the EM for GMM algorithm
 K-means ClusteringContains classes for the K-Means algorithm
 Kernel FunctionsContains classes for computing kernel functions
 Math FunctionsContains classes for computing math functions
 Moments of Low OrderContains classes for computing the results of the low order moments algorithm
 Multivariate Outlier DetectionContains classes for computing the multivariate outlier detection
 NormalizationContains classes for computing normalization algorithms
 Optimization SolversContains classes for optimization solver algorithms
 Principal Component AnalysisContains classes for computing the results of the principal component analysis (PCA) algorithm
 QR DecompositionContains classes for computing the results of the QR decomposition algorithm
 Quality MetricsContains classes for checking the quality of the classification algorithms
 QuantileContains classes to run the quantile algorithms
 Singular Value DecompositionContains classes to run the singular-value decomposition (SVD) algorithm
 SortingContains classes to run the sorting algorithms
 Univariate Outlier DetectionContains classes for computing results of the univariate outlier detection algorithm
 Base Classes
 Training and Prediction
 Classification
 Decision forest
 Decision tree
 Gradient Boosted TreesContains base classes of the gradient boosted trees algorithm
 Neural NetworksContains classes for training and prediction using neural network
 Recommendation Systems
 Regression
 Data ManagementContains classes that implement data management functionality, including NumericTables, DataSources, and Compression
 Data CompressionContains classes for data compression and decompression
 Data DictionariesContains classes that represent a dictionary of a data set and provide methods to work with the data dictionary
 Data ModelContains classes that provide functionality of Collection container for objects derived from SerializableBase
 Data Serialization and DeserializationContains classes that implement serialization and deserialization
 Data SourcesSpecifies methods to access data
 Numeric TablesContains classes for a data management component responsible for representation of data in the numeric format
 Numeric TensorsContains classes for a data management component responsible for representation of data in the n-dimensions numeric format
 ServicesContains classes that implement service functionality, including error handling, memory allocation, and library version information
 Extracting Version InformationProvides information about the version of Intel(R) Data Analytics Acceleration Library
 Managing MemoryContains classes that implement memory allocation and deallocation
 Managing the Computational EnvironmentProvides methods to interact with the environment, including processor detection and control by the number of threads

For more complete information about compiler optimizations, see our Optimization Notice.