By Richard J. Roiger
"Dr. Roiger does a superb task of describing in step-by-step aspect formulae concerned about numerous information mining algorithms, besides illustrations. moreover, his tutorials in Weka software program supply first-class grounding for college students in comprehending the underpinnings of laptop studying as utilized to info Mining. The inclusion of RapidMiner software program tutorials and examples within the e-book is usually a distinct plus because it is among the preferred information Mining software program structures in use today."
--Robert Hughes, Golden Gate college, San Francisco, CA, USA
Data Mining: A Tutorial-Based Primer, moment Edition offers a entire creation to info mining with a spotlight on version construction and checking out, in addition to on studying and validating effects. The textual content publications scholars to appreciate how information mining should be hired to resolve genuine difficulties and realize no matter if an information mining answer is a possible substitute for a selected challenge. basic facts mining options, innovations, and overview equipment are provided and carried out with the aid of famous software program instruments.
Several new themes were additional to the second one version together with an creation to special info and information analytics, ROC curves, Pareto raise 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 info, 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 during the tutorials depicting the data discovery strategy. this permits the reader greatest flexibility for his or her hands-on information mining experience.
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Extra info for Data Mining: A Tutorial-Based Primer, Second Edition
The more difficult tasks such as defining a suitable problem to solve, preparing the data, choosing a data mining strategy, and evaluating performanceare topics addressed in the remaining chapters of the text. The next section offers g uidelines to help us determine when data mining is an appropriate problemsolving strategy. 3 IS DATA MINING APPROPRIATE FOR MY PROBLEM? Making decisions about whether to use data mining as a problem-solving strategy for a particular problem is a difficult task.
A probabilistic-view definition of a good credit risk might look like this: • The mean annual income for individuals who consistently make loan payments on time is $45,000. • Most individuals who are good credit risks have been working for the same company for at least 5 years. • The majority of good credit risks own their own home. This definition offers general guidelines about the characteristics representative of a good credit risk. Unlike the classical-view definition, this definition cannot be directly applied to achieve an answer about whether a specific person should be given an unsecured loan.
Four general types of knowledge can be defined to help us determine when data mining should be considered. • Shallow knowledge is factual in nature. Shallow knowledge can be easily stored and manipulated in a database. Database query languages such as SQL are excellent tools for extracting shallow knowledge from data. • Multidimensional knowledge is also factual. However, in this case, data are stored in a multidimensional format. Online analytical processing (OLAP) tools are used on multidimensional data.