Introduction to Data Mining

Michael Steinbach,Pang-Ning Tan,Vipin Kumar

Verlag: Pearson Education, 2015
ISBN 10: 9332518653 / ISBN 13: 9789332518650
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Introduction to Data Mining is a comprehensive book for computer science undergraduates and professionals taking up a course in the computational process of discovering patterns in large sets of data. The book introduces students to the concepts of data mining, covering practical and theoretical aspects of the subject. It contains a large number of examples to illustrate the concepts, making it easier for students to put the theory in an application-based setting. In addition, the book includes instructor resources, giving lecturers access to online resources for exercises and complete set of lecture slides. The book is indispensable to all computer science engineers and statisticians. Table of Contents 1. Introduction 2. Data 3. Exploring Data 4. Classification: Basic Concepts, Decision Trees, and Model Evaluation 5. Classification: Alternative Techniques 6. Association Analysis: Basic Concepts and Algorithms 7. Association Analysis: Advanced Concepts 8. Cluster Analysis: Basic Concepts and Algorithms 9. Cluster Analysis: Additional Issues and Algorithms 10. Anomaly Detection Appendix B: Dimensionality Reduction Appendix D: Regression Appendix E: Optimization Printed Pages: 732. Buchnummer des Verkäufers 101448

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Bibliografische Details

Titel: Introduction to Data Mining
Verlag: Pearson Education
Erscheinungsdatum: 2015
Einband: Softcover
Zustand: New

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Rob Sullivan
Verlag: Springer-Verlag Gmbh Dez 2011 (2011)
ISBN 10: 1588299422 ISBN 13: 9781588299420
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Buchbeschreibung Springer-Verlag Gmbh Dez 2011, 2011. Buch. Zustand: Neu. Neuware - Data mining provides a set of new techniques to integrate, synthesize, and analyze tdata, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization. One of the major challenges for the scientific community, a challenge that has been seen in many business disciplines, is the exponential increase in data being generated by new experimental techniques and research. A single microarray experiment, for example, can generate thousands of data points that need to be analyzed, and this problem is predicted to increase. As new techniques in areas such as genomics and proteomics continue to be adopted into the mainstream as the costs fall, the need for effective mechanisms for synthesizing these disparate forms of data together for analysis is of paramount importance. But the sheer volume of data means that traditional techniques need to be augmented by approaches that elicit knowledge from the data, using automated procedures. Data mining provides a set of such techniques, new techniques to integrate, synthesize, and analyze the data, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization. 635 pp. Englisch. Artikel-Nr. 9781588299420

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S. N. Sivanandam
Verlag: Springer Berlin Heidelberg Sep 2006 (2006)
ISBN 10: 3540343504 ISBN 13: 9783540343509
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Buchbeschreibung Springer Berlin Heidelberg Sep 2006, 2006. Buch. Zustand: Neu. Neuware - This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. This book is intended for a wide audience of readers who are not necessarily experts in data warehousing and data mining, but are interested in receiving a general introduction to these areas and their many practical applications. Since data mining technology has become a hot topic not only among academic students but also for decision makers, it provides valuable hidden business and scientific intelligence from a large amount of historical data. It is also written for technical managers and executives as well as for technologists interested in learning about data mining. 852 pp. Englisch. Artikel-Nr. 9783540343509

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