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Extra resources for Enhance Your Business Applications: Simple Integration of Advanced Data Mining Functions
Again, this occurs in near real time since scoring happens on the fly when the customer contact takes place. This supports the bank personnel with the latest information at the time of customer interaction. 3, “Campaign management” on page 39). The business insights provided by the use of clustering for customer profiling enables the bank to offer specific, personalized services and products to its customers. In the commercial environment, clustering and profile scoring are used in the following areas among many others: Chapter 3.
If the model to be deployed is created in a data mining workbench, such as DB2 Intelligent Miner for Data, the model must be exported into a PMML file. Then you import the PMML file into DB2 UDB using a SQL script. If no mining model exists in the DB2 Intelligent Miner for Data workbench, you create the SQL script that uses the modeling API to create the model via the mining algorithm of IM Modeling. After the script is set up, another application or the batch scheduler can invoke it. Every time a mining run is invoked using the Chapter 2.
The deployment of scoring results is done with ease and with less intervention to make the results available. The scores must be updated at regular times for the obvious reason that the business environment changes all the time too. Repeatable and eventually automated (parts of) processes are the way to go for deployment. This way you can schedule batch runs for automated rescoring or recalibration of models, which is cost effective. Another interesting possibility when you have a repeatable process is regenerating the model based on new data or triggering a new model to be built when the old one no longer fits.