Download e-book for iPad: Data Mining: A Tutorial-Based Primer, Second Edition by Richard J. Roiger

By Richard J. Roiger

ISBN-10: 1051051061

ISBN-13: 9781051051067

ISBN-10: 1498763979

ISBN-13: 9781498763974

ISBN-10: 1498763987

ISBN-13: 9781498763981

"Dr. Roiger does an outstanding activity of describing in step-by-step element formulae excited by numerous information mining algorithms, in addition to illustrations. additionally, his tutorials in Weka software program offer very good grounding for college kids in comprehending the underpinnings of computing device studying as utilized to information Mining. The inclusion of RapidMiner software program tutorials and examples within the ebook can be a distinct plus because it is among the preferred info Mining software program systems in use today."

--Robert Hughes, Golden Gate college, San Francisco, CA, USA

Data Mining: A Tutorial-Based Primer, moment Edition offers a accomplished creation to info mining with a spotlight on version construction and checking out, in addition to on studying and validating effects. The textual content courses scholars to appreciate how information mining will be hired to unravel actual difficulties and realize no matter if a knowledge mining answer is a possible substitute for a selected challenge. primary information mining ideas, concepts, and evaluate equipment are offered and applied with assistance from recognized software program instruments.

Several new themes were extra to the second one version together with an advent to important info and information analytics, ROC curves, Pareto elevate charts, tools for dealing with large-sized, streaming and imbalanced info, help vector machines, and prolonged insurance of textual facts mining. the second one version comprises tutorials for characteristic choice, facing imbalanced info, outlier research, time sequence research, mining textual facts, and more.

The textual content offers in-depth assurance of RapidMiner Studio and Weka’s Explorer interface. either software program instruments are used for stepping scholars in the course of the tutorials depicting the data discovery strategy. this enables the reader greatest flexibility for his or her hands-on information mining experience.



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Additional info for Data Mining: A Tutorial-Based Primer, Second Edition

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Preface ◾ xxxvii USING WEKA AND RAPIDMINER Students are likely to benefit most by developing a working knowledge of both tools. This is best accomplished by students beginning their data mining experience with Weka’s Explorer interface. The Explorer is easy to navigate and makes several of the more difficult preprocessing tasks transparent to the user. Missing data are automatically handled by most data mining algorithms, and data type conversions are automatic. The format for model evaluation, be it a training/test set scenario or cross-validation, is implemented with a simple click of the mouse.

If a patient does not have swollen glands and has a fever, the diagnosis is a cold. • If a patient does not have swollen glands and does not have a fever, the diagnosis is an allergy. The decision tree tells us that we can accurately diagnose a patient in this data set by concerning ourselves only with whether the patient has swollen glands and a fever. The attributes sore throat, congestion, and headache do not play a role in determining a diagnosis. 1. 2 Data Instances with an Unknown Classification Patient ID 11 12 13 Sore Throat Fever Swollen Glands Congestion Headache Diagnosis No Yes No No Yes No Yes No No Yes No No Yes Yes Yes ?

Basic methods for evaluating the outcome of a data mining session are described. • Chapter 3 details a decision tree algorithm, the Apriori algorithm for producing association rules, a covering rule algorithm, the K-means algorithm for unsupervised clustering, and supervised genetic learning. Tools are provided to help determine which data mining techniques should be used to solve specific problems. Section II: Tools for Knowledge Discovery • Chapter 4 presents a tutorial introduction to Weka’s Explorer.

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Data Mining: A Tutorial-Based Primer, Second Edition by Richard J. Roiger

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