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IBM SPSS Modeler Cookbook

IBM SPSS Modeler Cookbook

By : Keith McCormick, Abbott
4.4 (20)
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IBM SPSS Modeler Cookbook

IBM SPSS Modeler Cookbook

4.4 (20)
By: Keith McCormick, Abbott

Overview of this book

IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork. IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art. Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace. Go beyond the basics and get the full power of your data mining workbench with this practical guide.
Table of Contents (11 chapters)
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10
Index

Data mining is a business process

Data mining by discovery and interpretation of patterns in data is:

  • The use of business knowledge
  • To create new knowledge
  • In natural or artificial form

The most important thing for you to know about data mining is that it is a way of using business knowledge.

The process of data mining uses business knowledge to create new knowledge, and this new knowledge may be in one of the two forms. The first form of new knowledge that data mining can create is "natural knowledge", that is, knowledge sometimes referred to as insight. The second form of new knowledge that data mining can create is "artificial knowledge", that is, knowledge in the form of a computer program, sometimes called a predictive model. It is widely recognized that data mining produces two kinds of results: insight and predictive models.

Both forms of new knowledge are created through a process of discovering and interpreting patterns in data. The most well-known type of data mining technology is called a data mining algorithm. This is a computer program that finds patterns in data and creates a generalized form of those patterns called a "predictive model". What makes these algorithms (and the models they create) useful is their interpretation in the light of business knowledge. The patterns that have been discovered may lead to new human knowledge, or insight, or they may be used to generate new information by using them as computer programs to make predictions. The new knowledge only makes sense in the context of business knowledge, and the predictions are only of value if they can be used (through business knowledge) to improve a business process.

Data mining is a business process, not a technical one. All data mining solutions start from business goals, find relevant data, and then proceed to find patterns in the data that can help to achieve the business goals. The data mining process is described well by the aforementioned CRISP-DM industry standard data mining methodology, but its character as a business process has been shaped by the data mining tools available. Specifically, the existence of data mining workbenches that can be used by business analysts means that data mining can be performed by someone with a great deal of business knowledge, rather than someone whose knowledge is mainly technical. This in turn means that the data mining process can take place within the context of ongoing business processes and need not be regarded as a separate technical development. This leads to a high degree of availability of business knowledge within the data mining process and magnifies the likely benefits to the business.

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