Mining. Pang-Ning Tan, Michigan State University, Vipin Kumar, University of Minnesota Preface to Provides both theoretical and practical coverage of all data mining topics. Introduction [PPT] [PDF] (last updated: 14 Feb, ). Data . N ING TAN. Michigan State . for the book. A survey of clustering techniques in data mining, originally Overview Specifically, this book provides a comprehensive introduction to data mining . and Vipin Kumar. In particular. Textbook: Introduction to Data Mining by Pang-Ning Tan,. Michael Steinbach, and Vipin Kumar, Data Mining: Concepts and Techniques by Jiawei.
|Language:||English, Spanish, Dutch|
|Distribution:||Free* [*Register to download]|
Textbook: – Introduction to Data Mining by Pang-Ning Tan,. Michael Steinbach, and Vipin Kumar, – Data Mining: Concepts and Techniques by Jiawei. Title: Introduction to Data Mining / Pang-Ning Tan, Michigan State University, of Minnesota, Anuj Karpatne, University of Minnesota, Vipin Kumar, University of. Introduction to Data Mining - by Pang-Ning Tan,. Michael Steinbach, and Vipin Kumar. Chapter 1: Introduction. Chapter 2: Data. Chapter 3: Exploring data.
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. Contents 1. Introduction 2.
Data 3. Exploring data 4. Association analysis: Cluster analysis: Anomaly detection App. Linear algebra App.
Dimensionality reduction App. Probability and statistics App. Regression App. Notes Includes bibliographical references and indexes. Textbook INFS View online Borrow download Freely available Show 0 more links Related resource Table of contents at http: Other links Inhaltsverzeichnis at http: Set up My libraries How do I set up "My libraries"?
These 26 locations in All: Rockhampton Campus Library. Albury-Wodonga Campus Library. Open to the public ; Bathurst Campus Library. Deakin University Library. Edith Cowan University Library. Open to the public. Flinders University Central Library. La Trobe University Library. Borchardt Library, Melbourne Bundoora Campus. Macquarie University Library.
Open to the public ; QA Monash University Library. Gardens Point Campus Library. Open to the public R N Sydney Library. It supplements the discussions in the other chapters with a discussion of the statistical concepts statistical significance, p-values, false discovery rate, permutation testing, etc. This chapter addresses the increasing concern over the validity and reproducibility of results obtained from data analysis.
The addition of this chapter is a recognition of the importance of this topic and an acknowledgment that a deeper understanding of this area is needed for those analyzing data. Classification: Some of the most significant improvements in the text have been in the two chapters on classification.
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. We begin t he technical discussion of this book with a. AILhough t. Chapter 3, on data exploration, discusses summary st. These techniques provide the means for quickly gaining insight into a data set. Chapters 4 and 5 cover classification. Chapter 4 provides a foundation by discussing decision t ree classifiers and several issues t.
Using this foundation, Chapter 5 describes a number of other important classification techniques: rule-based systems, nearest-neighbor classifiers, Bayesian classifiers, arti ficial neural networks, sup- port vector machines, and ensemble classifiers, which are collections of classi- 12 Chapter 1 Introduction fiers. The multiclass and imbalanced class problems are also discussed. These topics can be covered independently. Association analysis is explored in Chapters 6 and 7.
Chapter 6 describes the basics of association analysis: frequent itemsets, association rules, and some of the algorithms used to generate them. Specific types of frequent itemsets-maximal, closed, and hyperclique-that are important for data min- ing are also discussed, and the chapter concludes with a discussion of evalua- tion measures for association analysis.
Chapter 7 considers a variety of more advanced topics, including how association analysis can be applied to categor- ical and continuous data or to data that has a concept hierarchy. A concept hierarchy is a hierarchical categorization of obj ects, e.
This chapter also describes how assoc1at ion analysis can be extended to find sequential patterns patterns involving order , patterns in graphs, and negative relationships if one item is present, then t he other is not. Cluster analysis is discussed in Chapters 8 and 9. Chapter 8 first describes the different types of clusters and then presents three specific clustering tech- niques: K-means, agglomerative hierarchical cluster ing, and DBSCAN.
This is followed by a discussion of techniques for validating the results of a cluster- ing algorithm. Additional clustering concepts and techniques are explored in Chapter 9, incl uding fuzzy and probabi listic clustering, Self-Organizing Maps SOM , graph-based clustering, and density-based clustering.
There is also a discussion of scalability issues and factors to consider when selecting a clus- tering algorithm. The last chapter, Chapter 10, is on anomaly detection.