By Richard J. Roiger
"Dr. Roiger does a good activity of describing in step-by-step element formulae excited about quite a few info mining algorithms, in addition to illustrations. additionally, his tutorials in Weka software program supply very good grounding for college kids in comprehending the underpinnings of laptop studying as utilized to info Mining. The inclusion of RapidMiner software program tutorials and examples within the ebook is additionally a distinct plus because it is likely one of the preferred facts Mining software program structures in use today."
--Robert Hughes, Golden Gate collage, San Francisco, CA, USA
Data Mining: A Tutorial-Based Primer, moment Edition presents a accomplished advent to facts mining with a spotlight on version development and trying out, in addition to on analyzing and validating effects. The textual content courses scholars to appreciate how info mining might be hired to resolve genuine difficulties and realize even if a knowledge mining answer is a possible substitute for a selected challenge. basic information mining recommendations, ideas, and overview tools are awarded and carried out with the aid of famous software program instruments.
Several new subject matters were further to the second one variation together with an creation to important facts and knowledge analytics, ROC curves, Pareto elevate charts, tools for dealing with large-sized, streaming and imbalanced information, aid vector machines, and prolonged assurance of textual info mining. the second one variation includes tutorials for characteristic choice, facing imbalanced facts, outlier research, time sequence research, mining textual facts, and more.
The textual content presents in-depth assurance of RapidMiner Studio and Weka’s Explorer interface. either software program instruments are used for stepping scholars throughout the tutorials depicting the information discovery method. this permits the reader greatest flexibility for his or her hands-on facts mining experience.
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Additional info for Data Mining: A Tutorial-Based Primer, Second Edition
Let’s take a moment to define and illustrate each view. 1 The Classical View The classical view attests that all concepts have definite defining properties. These properties determine if an individual item is an example of a particular concept. The classicalview definition of a concept is crisp and leaves no room for misinterpretation. This view supports all examples of a particular concept as being equally representative of the concept. Here is a rule that employs a classical-view definition of a good credit risk for an unsecured loan: IF Annual Income ≥ $45,000 and Years at Current Position ≥ 5 and Owns Home = True THEN Good Credit Risk = True Data Mining: A First View ◾ 7 The classical view states that all three rule conditions must be met for the applicant to be considered a good credit risk.
There are at least three problems with this approach. First, computation times will be a problem when the classification table contains thousands or millions of records. Second, the approach has no way of differentiating between relevant and irrelevant attributes. Third, we have no way to tell whether any of the chosen attributes are able to differentiate the classes contained in the data. The first problem is prohibitive when we compare this approach to that of building a generalized classification model such as a decision tree.
As we can see, the decision tree has generalized the data and provided us with a summary of those attributes and attribute relationships important for an accurate diagnosis. 2. • Since the patient with ID = 11 has a value of yes for swollen glands, we follow the right link from the root node of the decision tree. The right link leads to a terminal node, indicating that the patient has strep throat. • The patient with ID = 12 has a value of no for swollen glands. We follow the left link and check the value of the attribute fever.
Data Mining: A Tutorial-Based Primer, Second Edition by Richard J. Roiger