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Building Data Mining Applications for Crm

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Building Data Mining Applications for Crm

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  • March 24, 2010
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Page 1 of 60
Building Data Mining Applications for CRM

Introduction
This overview provides a description of some of the most common data mining algorithms in use today.   We have broken the discussion into two sections, each with a specific theme: • Classical Techniques: Statistics, Neighborhoods and Clustering • Next Generation Techniques: Trees, Networks and Rules Each section will describe a number of data mining algorithms at a high level, focusing on the "big picture" so that the reader will be able to understand how each algorithm fits into the landscape of data mining techniques.   Overall, six broad classes of data mining algorithms are covered.  Although there are a number of other algorithms and many variations of the techniques described, one of the algorithms from this group of six is almost always used in real world deployments of data mining systems. I. Classical Techniques: Statistics, Neighborhoods and Clustering 1.1. The Classics

These two sections have been broken up based on when the data mining technique was developed and when it became technically mature enough to be used for business, especially for aiding in the optimization of customer relationship management systems.  Thus this section contains descriptions of techniques that have classically been used for decades the next section represents techniques that have only been widely used since the early 1980s. This section should help the user to understand the rough differences in the techniques and at least enough information to be dangerous and well armed enough to not be baffled by the vendors of  different data mining tools. The main techniques that we will discuss here are the ones that are used 99.9% of the time on existing business problems.  There are certainly many other ones as well as proprietary techniques from particular vendors - but in general the industry is converging to those techniques that work consistently and are understandable and explainable. 1.2. Statistics

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