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Books by Vipin Kumar (Author of Introduction to Data Mining)

Vipin Kumar has 31 books on Goodreads with 2150 ratings. Vipin Kumar's most popular book is Introduction to Data Mining.

Introduction to Data Mining - University of Minnesota

Avoiding False Discoveries: A completely new addition in the second edition is a chapter on how to avoid false discoveries and produce valid results, which is novel among other contemporary textbooks on data mining. It supplements the discussions in the other chapters with a discussion of the statistical concepts (statistical significance, p-values, false discovery rate, permutation testing ...

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Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining.

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Why Mine Data? Scientific Viewpoint OData collected and stored at enormous speeds (GB/hour) – remote sensors on a satellite – telescopes scanning the skies – microarrays generating gene

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Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics.

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An Introduction to Data Mining Kurt Thearling, Ph.D. 2 Outline — Overview of data mining — What is data mining? — Predictive models and data scoring — Real-world issues — Gentle discussion of the core algorithms and processes — Commercial data mining software applications — Who are the players?

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Introduction to Data Mining Instructor's Solution Manual Pang-Ning Tan Michael Steinbach ... Discuss whether or not each of the following activities is a data mining task. (a) Dividing the customers of a company according to their gender. ... 2 Chapter 1 Introduction area of data mining known as predictive modelling. We could use

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Free data mining books. An Introduction to Statistical Learning: with Applications in R Overview of statistical learning based on large datasets of information. The exploratory techniques of the data are discussed using the R programming language. Modeling With Data This book focus some processes to solve analytical problems applied to data.

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Cluster analysis is used in data mining and is a common technique for statistical data analysis used in many fields of study, such as the medical & life sciences, behavioral & social sciences, engineering, and in computer science.

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Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics.

Pang-Ning Tan (Author of Introduction to Data Mining)

Pang-Ning Tan is the author of Introduction to Data Mining (4.00 avg rating, 301 ratings, 18 reviews, published 2005), Advances in Knowledge Discovery an...

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Data mining. Summary "Introduction to Data Mining is a complete introduction to data mining for students, researchers, and professionals. It provides a sound understanding of the foundations of data mining, in addition to covering many important advanced topics."--BOOK JACKET. Contents. 1. Introduction 2. Data 3. Exploring data 4.

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Introducing the fundamental concepts and algorithms of data mining Introduction to Data Mining, 2nd Edition, gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and professionals.

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We used this book in a class which was my first academic introduction to data mining. The book's strengths are that it does a good job covering the field as it was around the 2008-2009 timeframe. Included are discussions of exploring data, classification, clustering, association analysis, cluster analysis, and anomaly detection.

Data mining ebook collection: Introduction to Data Mining

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