Step-by-Step Guide to Data Mining Process
Users without prior experience in building predictive models can rely on IBM SPSS Modeler to guide you through the data mining process from data access and preparation to modeling, reporting and deployment. Based on the Cross-Industry Standard Process for Data Mining (CRISP-DM), IBM SPSS Modeler compartmentalizes the relevant techniques into a comprehensive toolkit for each stage of the data mining process.
Import data from a wide range of databases, spreadsheets and flat files including IBM SPSS Statistics and Microsoft Excel files.
Save time by using the automated data preparation, and have the flexibility of manual adjustment through a wide array of data preparation techniques.
Apart from the traditional statistical tools, you can tap on powerful machine-learning and artificial intelligence techniques to build more accurate models.
Build Predictive Models Quickly and Intuitively
Designed for business users without any programming knowledge, IBM SPSS Modeler provides highly intuitive graphical interface that enables users to visualize and perform every step of the data mining process. Powerful automation tools such as automated data preparation and automatic modeling make it easy to prepare data for analysis, find the best model based on hidden patterns in the data and quickly produce consistent and accurate results.
Discover Hidden Patterns and Insights
IBM SPSS Modeler employs models built with powerful machine learning and artificial intelligence techniques, enabling you to discover hidden patterns in your data.
Make predictions or forecasts based on historical data.
Group people or detect unusual patterns with automatic clustering, anomaly detection and clustering neural network techniques.
Discover associations, links or sequences using Apriori, CARMA and sequential association.
Sequential association algorithm for order-sensitive analyses.
Interactive Visualization for Clearer Insight:
IBM SPSS Modeler offers new interactive visualizations for key algorithms and ‘ensemble’ models, making results easier to understand and communicate.