Frontline Systems has released XLMiner SDK V2018, a new version of its software development kit for data mining, text mining, forecasting, and predictive analytics. XLMiner SDK offers application developers working in C++, C#, Java, Python or R an API for creating applications that use predictive analytics.
XLMiner SDK provides full API support for C++ 11 or later, C# 4.0 or later, Java 8, Python 2.7 or 3.6 (both are supported), and R 3.4.
In Microsoft Visual Studio and R Studio, developers will benefit from automatic recognition and “command completion” for XLMiner’s objects, properties and methods. The new SDK is also ready for REPL (Read-Eval-Print-Loop) style execution with C# Interactive.
XLMiner SDK’s R support uses R-native types, including R’s own DataFrame type so it can be used easily with a range of R packages from CRAN. XLMiner SDK provides its own “R package” that can be loaded with one command from R itself, or an IDE such as R Studio.
According to Frontline, for C++, C# and Java developers, XLMiner SDK should be especially welcome, since quality data mining tools have been hard to find for these popular languages, and R and Python developers will also find that XLMiner SDK offers a comprehensive data mining and text mining toolkit.
The SDK also handles unstructured text data, and provides stemming, term normalization, vocabulary reduction, creation of a term-document matrix, and concept extraction with latent semantic indexing. It even has built-in facilities to draw a statistically representative sample from an Apache Spark Big Data cluster, running a Frontline-supplied component on one of the cluster nodes.
The new SDK release can export a variety of trained/fitted models in industry-standard PMML (Predictive Modeling Markup Language) format, from data transformations to linear and logistic regression, decision trees, neural networks, and k nearest neighbors for both classification and prediction; discriminant analysis, naïve Bayes, time series models, association rules, and even ensembles with boosting, bagging, and random forest methods. Few other products provide such extensive PMML support.
XLMiner SDK also provides its own JSON serialization format, more general than PMML, for its full range of objects (DataFrames, Estimators and Models) and properties.
Statistical and machine learning algorithms in XLMiner SDK are optimized for performance on current Intel-compatible processors. In the new release, the Naive Bayes algorithm is much faster and less memory-intensive, while K Nearest Neighbors is an order of magnitude faster in k-parameter tuning, and handles distance matrices that would exceed available memory in other software.
Category Reduction and Missing Data Handling algorithms are also extended for multivariate use, with new "missing value options" for different data types, and One-Hot-Encoding is faster and enhanced for categorical variables. Additionally, the new release offers Vector and Matrix objects that enable developers to write high-level "linear algebra expressions" with high-performance, parallel multi-core execution.
XLMiner SDK V2018 is available now for both 32-bit and 64-bit Windows. Developers can register for a free account at www.solver.com, and download and install a fully-functional version of XLMiner SDK with a free 15-day trial license.