XLMiner provides everything you need to sample data from many sources — PowerPivot, Microsoft/IBM/Oracle databases, or spreadsheets; explore and visualize your data with multiple linked charts; preprocess and ‘clean’ your data, fit data mining models, and evaluate your models’ predictive power.
XLMiner Data Mining Add-in For Excel
XLMiner is the only comprehensive data mining add-in for Excel, with neural nets, classification and regression trees, logistic regression, linear regression, Bayes classifier, K-nearest neighbors, discriminant analysis, association rules, clustering, principal components, and more.
XLMiner provides everything you need to sample data from many sources — PowerPivot, Microsoft/IBM/Oracle databases, or spreadsheets; explore and visualize your data with multiple linked charts; preprocess and ‘clean’ your data, fit data mining models, and evaluate your models’ predictive power.
Advanced Data Mining, The Ease of Excel, and Competitive PricingComprehensive set of data preparation features to import and clean your data including:
Sample data from virtually any database, including Microsoft’s PowerPivot in-memory database handling 100 million rows
Clean your data with a comprehensive set of data handling utilities including categorizing data and handling missing values
Partition your data into training, validation, and test datasets
Powerful tools for analysis and prediction including:
Use visualization aids from simple bar, line and histogram charts to multiple linked charts, one-click changes to axes, colors and panels, zooming, brushing and more
Use a range of supervised and unsupervised learning techniques for both continuous and categorical data
Use both classical methods like MLR and logistic regression, and data mining methods like CART and neural networks, and compare their predictive power
Built in time series analysis tools including:
Analyze time series data using ACF/PACF plots and smoothing techniques
Use a broad range of models including exponential smoothing, ARIMA, and standard and seasonalmodels
Easily use each model to forecast future values
Available for Excel 2007 / 2010 / 2013In summary, XLMiner includes:
Powerful data exploration and visualization features, in additional to its data preparation, data mining, and time series forecasting methods.
Support for Microsoft’s PowerPivot add-in, which handles ‘Big Data’ and integrates multiple, disparate data sources into one in-memory database inside Excel.
Support for Excel 2013 Preview Edition, and the new PowerPivot add-in that ships with the new Excel.
XLMiner Professional edition is designed for practitioners and researchers who deal with large datasets.
XLMiner Capabilities Overview
XLMiner provides a comprehensive set of analysis features based both on statistical and machine learning methods. A problem or a data set can be analyzed by several methods. It is usually a good idea to try different approaches, compare their results, and then choose a model that suits the problem well.Databases, Spreadsheets and Size LimitsXLMiner can work with large data sets which may exceed the limits in Excel. A standard procedure is to sample data from a larger database, bring it into Excel to fit a model, and, in the case of supervised learning routines, score output back out to the database. XLMiner can sample data from Oracle, SQL Server and Access databases, and in V4.0 from PowerPivot in-memory databases.In XLMiner V4.0 (unlike earlier XLMiner versions), this feature is available in both the Professional edition and the Educational edition. However, the Educational edition limits the size of the database table or view from which you can sample, as well as the size of the sample drawn. Data Exploration and VisualizationXLMiner V4.0 has built-in features for data exploration and visualization. It is no longer necessary to use external tools such as Tableau or SpotFire to visualize your data; this can be done easily and at no extra cost in XLMiner itself. You can create any number of chart windows (each containing multiple linked charts), name, and save these windows in the workbook. In a chart window, you can create bar, line, scatterplot, boxplot, and histogram charts with one click. Also available (with one click) are quick charts of all variables, scatterplot matrix charts, and parallel coordinates charts.You can use your mouse to zoom in and out, or select points of interest in a given chart; these points will be 'brushed' or highlighted in other charts in the same chart window, and the actual values of variables at each highlighted point are visible in scrollable side pane. You can use filters for each continuous and categorical variable -- simple sliders and checkbox arrays that appear automatically in a side pane -- to include or exclude points of interest. With a simple point and click, you can change axes or colors, or create multiple panels based on the values of any categorical variable.Exploring data in this fashion often yields quick insights about hidden relationships in the data, as well as "what is important, and what is not." This step can inform your choices of further operations, from data preparation and transformation to the fitting of data mining models. OperationsThere are five broad groups of operations in XLMiner:PartitioningA data set with known values of an outcome (response) variable is necessary to train a data mining model. For training a model, we usually choose (at random) a fraction of the available data -- the training partition. Trained models can then be applied to another partition -- the validation partition -- of the same data set to see how well they do with data that they were not trained with. In this phase, models can be adjusted and the best performing model selected. After a final model is selected, it can be applied to a third partition -- the test partition -- to test how well the final model will do with data that have been used neither in testing nor in validation.XLMiner also supports partitioning with oversampling, used when rare events are modeled and you need to assure an adequate supply of those events in the modeling process. click a link below to learn more:
Partitioning
Partitioning with oversampling
ClassificationWhen the outcome variable is discrete or categorical, the objective of the data mining exercise is to classify the records into the discrete classes or categories.XLMiner offers several techniques for classification:
Discriminant Analysis
Logistic Regression with best subset selection
Classification Trees
Naive Bayes Classifier
Neural Networks
k-Nearest Neighbors
PredictionWhen the outcome variable is continuous, the objective is to predict the value of the outcome variable for each of the data records.XLMiner offers the following methods of prediction:
Multiple Linear Regression with best subset selection
k-Nearest Neighbors
Regression Trees
Neural Networks
Affinity AnalysisSome problems involve detecting association among the properties of data records. XLMiner supports generation of Association Rules for showing which attributes of the data occur frequently together. One common application is to determine groups of products customers are likely to buy together, also known as Market Basket Analysis.Time SeriesXLMiner offers time series forecasting, with the exploratory techniques ACF (Autocorrelation function) and PACF (Partial autocorrelation function), smoothing techniques (moving average, exponential, double exponential and Holt-Winters), as well as ARMA and ARIMA modeling.Data Reduction and ExplorationIt is often useful or necessary to reduce the dimensionality of data into only a few attributes that matter more than others. In this situation, we do not attempt to classify or predict an outcome variable. Instead, the objective is to discover similarities in records and group them together using the available attributes (variables).One such method involves deciding which variables matter most in explaining differences among records. Other methods categorize data into clusters that can be represented as a new categorical variable added to the data.XLMiner supports the following methods of data exploration and reduction:
Principal Components Analysis
k-Means Clustering
Hierarchical Clustering
Output presentation and graphicsXLMiner provides special graphics to enhance the understanding of the data and the analysis outcomes. For instance, tree diagrams in classification and regression trees, and dendrograms in hierarchical clustering give very useful insights.In conjunction with XLMiner outputs, you can use Excel's built-in features to work with the output. For instance, histograms, scatter plots and bubble plots are very useful to provide an insight into the data and the fitted outcomes. Lift charts and gain charts can be easily generated from XLMiner outputs to see the benefit produced by the data mining exercise.
XLMiner offers a full suite of techniques for classification, prediction, affinity analysis, data exploration, and data reduction. It is in use at:
Northrup Grumman
National Institutes of Health
Westinghouse (Savannah River)
JD Powers
NASA
Bell Atlantic
Pitney Bowes
Centers for Disease Control
Monsanto
ExxonMobil
US Army
FDA
Experion
National Inst. of Standards & Technology
Fidelity Investments
Northrup Grumman
Blackstone Group
and more.
XLMiner is the only comprehensive data mining and forecasting add-in for Excel, with neural nets, classification and regression trees, logistic regression, linear regression, Bayes classifier, K-nearest neighbors, discriminant analysis, association rules, clustering, principal components, exponential smoothing and ARIMA models, and more. It also offers rich charting facilities for rapid data exploration and visualization. XLMiner has special features to sample data from external SQL databases and the Microsoft PowerPivot in-memory database. It also includes utilities for data partitioning, missing data handling, binning, categorical data transformation.Evaluation Versions. If you want to "try before you buy," simply contact us at sales@unitedaddins.com, and we will arrange that you download and run the XLMiner Setup program. You'll be able to use a full featured, full capacity, full speed version of the software for 15 days, free of charge.What's Included. XLMiner supports Excel 2013 Preview Edition and Excel 2010 (32-bit and 64-bit), Excel 2007, and Excel 2003 on Windows 7, Windows Vista, Windows XP, and Windows Server 2008. It comes with a comprehensive User Guide and extensive online Help.Technical Support. Installation assistance and basic technical support for XLMiner is included for the term of your license. If you need consulting assistance to choose and apply data mining methods for your problem, this is available at an extra charge.Please contact us at sales@unitedaddins.com to get a quote.
