By Richard J. Roiger
"Dr. Roiger does a great task of describing in step-by-step aspect formulae taken with a number of info mining algorithms, besides illustrations. moreover, his tutorials in Weka software program supply 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 publication is usually a distinct plus because it is among the preferred information 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 complete advent to info 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 may be hired to unravel actual difficulties and realize even if an information mining answer is a possible substitute for a selected challenge. basic facts mining concepts, recommendations, and overview tools are offered and carried out with the aid of recognized software program instruments.
Several new issues were extra to the second one version together with an creation to special facts and information analytics, ROC curves, Pareto carry charts, tools for dealing with large-sized, streaming and imbalanced facts, help vector machines, and prolonged insurance of textual info mining. the second one variation includes tutorials for characteristic choice, facing imbalanced facts, outlier research, time sequence research, mining textual information, 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 information discovery procedure. this enables 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 name for this type of learning is inductionbased supervised concept learning or just supervised learning. The purpose of supervised learning is twofold. First, we use supervised learning to build classification models from sets of data containing examples and nonexamples (has legs, so is not a plant) of the concepts to be learned. Each example or nonexample is known as an instance of data. Second, once a classification model has been constructed, the model is used to determine the classification of newly presented instances of unknown origin.
In addition, if this is indeed an incorrect diagnosis, adding the new instance to the classification table will continue to propagate classification error. To be sure, we are making an assumption that the instances in the training data used to build the decision tree accurately represent the population in general. With this assumption, the correct diagnosis for the new patient is allergy. A variation of this approach known as a k-nearest neighbor classifier classifies a new instance with the most common class of its k nearest neighbors.
A list of definitions for these terms is provided at the end of each chapter. • End-of-chapter exercises. The end-of-chapter exercises reinforce the techniques and concepts found within each chapter. The exercises are grouped into one of three categories—review questions, data mining questions, and computational questions. • Review questions ask basic questions about the concepts and content found within each chapter. The questions are designed to help determine if the reader understands the major points conveyed in each chapter.