XLMiner Online Help

This help file will guide you in using XLMiner to analyze your business data. As you go through the file, you will get familiar with all the operations necessary for analyzing data with XLMiner.

To read about a specific technique, click on the relevant link below. Each topic is explained with a brief introduction, followed by an example of how to use XLMiner to utilize the procedure. Also, you may be interested on our FAQ page.

Table of Contents

1. Procedures 

• Partition Data
  • Standard Partition
  • Partition with Oversampling
• Data Utilities
  • Sample from Worksheet
  • Sample from Database (Only in Standard and Demo edition. Not present in Education Edition)
  • Missing Data Handling
  • Bin Continuous Data
  • Transform categorical Data
• Time Series 

• Prediction 
  • Multiple Linear Regression
  • k-Nearest Neighbors
  • Regression Tree
  • Neural Network (Multilayer feedforward)
• Classification
  • Discriminant Analysis
  • Logistic Regression
  • Classification Tree
  • Naive Bayes
  • Neural Network (Multilayer feedforward)
  • k-Nearest Neighbors
• Stored Model Sheets

• Affinity
  • Association Rules
• Data Reduction and Exploration
  • Principal Component Analysis
  • k-Means Clustering
  • Hierarchical Clustering
• Charts
  • Box Plot
  • Histogram
  • Matrix Plot

2. Data Handling Specifications

3. Scoring Test Data

4. References