predictive solutions

The IBM SPSS Modeler is a powerful analytical engine with a large range of algorithms offered. It allows to upload data from different sources. It allows to modify data, manage their quality, and treat gaps and deficiencies in the data. The PS CLEMENTINE Pack is a set of new, original procedures that significantly facilitate your work with the data. Moreover, it offers integration of the program with the repository.

The IBM SPSS Modeler is a system focusing a proven methodology of data mining project management (CRISP-DM) and advanced modeling techniques based on statistical procedures and artificial intelligence in one environment. The IBM SPSS Modeler is a system recognized all over the world by professionals specializing in data mining.

Storing and sharing results of your work with others may now be carried out in one place, thanks to the PS CLEMENTINE Database and the PS CLEMENTINE Manager managing application. Using access right assignment and object editing, you can limit or expand the group of people authorized to view and edit specific resources.

The PS CLEMENTINE Dispatcher allows for an automation of analytical processes that may be executed in the background mode and triggered as a result of a specific action. It is possible to launch the process automatically, at a pre-defined time, on a pre-defined day, and to define recurrent triggering. Furthermore, the process may be launched when a file appears in a specific directory, or using Web Service at the request of the user whose computer is not equipped with an analytical engine or with the PS CLEMENTINE PRO solution.

CRISP-DM (Cross-Industry Standard Process for Data Mining) is a project implementation methodology and data mining process model that provides a complete and universal map for projects aimed to create business solutions using data mining techniques. PS CLEMENTINE PRO provides full support of this model and organises work. 

• Understanding business - tasks in this step involve understanding business conditions, mapping business goals onto analytical targets, and preparing a project plan.
• Understanding data - the aim of this step is to identify data sources, get familiar with them, and understand their meaning for the business.
• Preparing data - tasks in this step lead to creation of final datasets that conform to format requirements, measurement scale, or cleanliness for modelling, and their description for clear interpretation of models. 
• Evaluation - the aim of this step is to verify models in light of business criteria; select the best models for distribution; and make decisions about the next steps and changes in future iterations of the process.
• Distribution of results - This key stage is to provide recipients with results, which could simply mean the delivery of a report. In data mining projects, however, it more often involves additional information being written in databases or use of models to provide recipients with e.g. on-line business recommendations.