We've released XLMiner SDK V2018, a next-generation version of our Software Development Kit for data mining, text mining, forecasting, and predictive analytics. This SDK offers application developers a powerful, high-level API for quickly creating applications that use predictive analytics. 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. But even R and Python developers will find that XLMiner SDK offers a far better integrated, comprehensive data mining and text mining toolkit.
Full Support for Popular Programming Languages
XLMiner SDK provides full API support for five popular programming languages: 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. And the new SDK is 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; hence it can be used easily with a wide 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.
Support for Popular Databases and Files, Text, and Big Data
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.
Model Export in PMML, and Export/Import in JSON
The new SDK release can export a wide range 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.
Faster and More Robust Algorithms
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. The new release even offers Vector and Matrix objects that enable developers to write high-level "linear algebra expressions" with high-performance, parallel multi-core execution.
Free Trial Version Available for Download
XLMiner SDK V2018 is available now for both 32-bit and 64-bit Windows. To try it out, just register for a free account and download and install the software with a free 15-day trial license. We're excited to see what our customers do with this powerful new release!