Forecasting, data mining, text mining in Excel
XLMiner® SDK Platform
Capabilities:
Uncover new revenue, predict churn or fraud
Explore, partition, transform data, extract features
Use regression, trees, neural nets, ensembles, PMML
Use with C++, C#, Java, Python or R
XLMINER SDK
FEATURES
ALGORITHMS
PRO vs PLATFORM
Use forecasting, data mining and text mining models in your application
XLMiner SDK offers developers working in C++, C#, Java, Python or R a powerful, high-level API for predictive analytics.
Data Exploration and Feature Selection
Pipeline Multiple Operations
Unsupervised Machine Learning
Run on 32-bit or 64-bit Windows
Classification and Prediction Algorithms
Exploit multiple cores automatically
Time Series Forecasting Methods
Run on desktop, server, cloud
DataFrame, Estimator, Model Objects
Connect to Spark Big Data clusters
Everything You Need to Develop and Deploy Powerful Predictive Analytics Applications
Common, Easy-to-Use Object-Oriented API
Use the same high-level objects (like DataFrame, Estimator and Model), properties and methods across different programming languages.
Parallelized Data Mining Algorithms
Exploit multiple processor cores using XLMiner SDK's built-in parallelized algorithms, without having to write parallel code yourself.
Thread-safe for Multi-user Applications
Call XLMiner SDK on multiple concurrent threads -- making it easy to build Web server and other applications that handle multiple clients concurrently.
Example Applications in Multiple Languages
Source code is included in each supported programming language for example applications, illustrating how to use the full range of XLMiner SDK features.
Help and Support
Our xxx-page XLMiner SDK User Guide and our highly-regarded technical support supports your efforts.
Straightforward Runtime Licensing
Obtain licenses to distribute XLMiner SDK with your application, or use it on your on-premise or cloud-based server(s), with simple terms that work for your application.
A Full Range of Forecasting, Data Mining, and Text Mining Algorithms
Fast Text Processing
Use stemming, term normalization, and vocabulary reduction to create a term-document matrix with weightings.
Latent Semantic Indexing
Extract concepts from text, representing common clusters of words in documents to create a concept-document matrix.
Unsupervised machine learning
Use k-Means Clustering and Hierarchical Clustering to structure rows, and Principal Components to reduce columns.
Powerful Feature Selection
Use filter, wrapper and embedded methods to identify variables with the greatest predictive power.
Exponential Smoothing and ARIMA models
Use exponential smoothing and ARIMA (Auto-Regressive Integrated Moving-Average) methods, with or without seasonality.
Linear and Logistic Regression
Use multiple linear regression and logistic regression, with automatic handling of categorical variables, automatic variable screening, and forward, backward, stepwise and exhaustive variable selection methods.
Ensembles of "Weak Learners"
Use boosting, bagging, and "random forest" methods to combine results from many instances of different algorithms.
Classification and Regression Trees
Use regression-based "decision trees" for both classification and prediction tasks.
Neural Networks
Use multi-layer feedforward neural networks for both classification and prediction tasks.
Discriminant Analysis and Naive Bayes
Use these classic, but surprisingly powerful methods for classification.
Association Rules
Automatically construct sets of rules for "market basket" and recommendation systems.
Compare XLMiner SDK Platform and XLMiner SDK Pro
The comparison chart below summarizes the differences between XLMiner SDK Platform and XLMiner SDK Pro.
We're happy to assist with any questions, to help make sure you find the right product for your unique needs.
