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Data Mining: Introductory And Advanced Topics. Front Cover. Margaret H Dunham. Pearson Education, - pages. 10 Reviews. Thorough in its coverage from basic to advanced topics, this book presents the key algorithms and techniques used in data mining. An emphasis is placed on. Download Data Mining: Introductory and Advanced Topics free ebook (pdf,epub, mobi) by Margaret H. Dunham.
About this book Introduction This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation.
The introductory chapter uses the decision tree classifier for illustration, but the discussion on many topics—those that apply across all classification approaches—has been greatly expanded and clarified, including topics such as overfitting, underfitting, the impact of training size, model complexity, model selection, and common pitfalls in model evaluation.
Almost every section of the advanced classification chapter has been significantly updated.
The material on Bayesian networks, support vector machines, and artificial neural networks has been significantly expanded. We have added a separate section on deep networks to address the current developments in this area.
The discussion of evaluation, which occurs in the section on imbalanced classes, has also been updated and improved.
Anomaly Detection: Anomaly detection has been greatly revised and expanded.
The reconstruction-based approach is illustrated using autoencoder networks that are part of the deep learning paradigm. Association Analysis: The changes in association analysis are more localized.
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Margaret H. Introductory and Advanced Topics , Techniques 2nd edition. Concepts and Techniques Introductory and Advanced Topics by Dunham, Topics by Bickel and Doksum Course code: Data mining introductory and advanced topics.
New Jersey: Prentice Hall. Fayyad, U.
Pang-Ning Tan, M. Introduction to Data Mining.
Introductory and Advanced Topics , Dunham M. For Analytics Credits: Course Plan: Han, Kamber, Download our data mining introductory and advanced topics eBooks for free and learn more about data mining introductory and advanced topics.
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