By Mamdouh Refaat
Are you an information mining analyst, who spends as much as eighty% of a while assuring info caliber, then getting ready that information for constructing and deploying predictive types? And do you discover plenty of literature on info mining idea and ideas, but if it involves useful suggestion on constructing sturdy mining perspectives locate little “how to” info? And are you, like so much analysts, getting ready the knowledge in SAS?
This publication is meant to fill this hole as your resource of sensible recipes. It introduces a framework for the method of knowledge training for facts mining, and offers the specific implementation of every step in SAS. additionally, enterprise purposes of information mining modeling require you to accommodate numerous variables, mostly hundreds and hundreds if no longer millions. for that reason, the booklet devotes a number of chapters to the equipment of information transformation and variable selection.
- A whole framework for the information coaching strategy, together with implementation information for every step.
- The entire SAS implementation code, that is simply usable through specialist analysts and knowledge miners.
- A targeted and finished process for the remedy of lacking values, optimum binning, and cardinality reduction.
- Assumes minimum skillability in SAS and contains a quick-start bankruptcy on writing SAS macros.
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Additional resources for Data Preparation for Data Mining Using SAS (The Morgan Kaufmann Series in Data Management Systems)
5. 3. Methods of variable reduction using X 2 , Gini, and Entropy variance methods, Chapter 17. 4 Neural Networks Neural networks are powerful mathematical models suitable for almost all data mining tasks, with special emphasis on classiﬁcation and estimation problems. 4 Neural Networks 23 their origins in attempts to simulate the behavior of brain cells, but that is where the relationship ends. There are numerous formulations of neural networks, some of which are specialized to solve speciﬁc problems, such as self-organizing maps (SOM), which is a special formulation suitable for clustering.
A logical condition in SAS macros must compare two numerical values. In fact, it compares two integer values. , contains numbers to the right of the decimal point), we must use the function %SYSEVALF() to enclose the condition. For example, let us assign noninteger values to A and B and rewrite the code. 5; %IF %SYSEVALF(&A>&B) %THEN %LET C=A; %ELSE %LET C=B; The syntax for the %DO–%WHILE and %DO–%UNTIL loops is straightforward. The following examples show how to use these two statements. %macro TenIterations; /* This macro iterates 10 times and writes the iteration number to the SAS Log.
Many good textbooks provide the details of the business aspect of data mining and how modeling ﬁts in the general scheme of things. Similarly, numerous good texts are dedicated to the explanation of the different data mining algorithms and software. We will not dwell much on these two areas. 1 depicts the typical stages of data ﬂow. In this process, many of the steps may be repeated several times in order to ﬁt the ﬂow of operations within a certain data mining methodology. The process can be described as follows.
Data Preparation for Data Mining Using SAS (The Morgan Kaufmann Series in Data Management Systems) by Mamdouh Refaat