Introduction to Data Mining

Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics.

Author: Pang-Ning Tan

Publisher:

ISBN: 9780273769224

Category: Data mining

Page: 864

View: 494

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. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.


Introduction to Data Mining Pearson New International Edition PDF eBook

Quotes This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts.

Author: Pang-Ning Tan

Publisher: Pearson Higher Ed

ISBN: 1292038551

Category: Computers

Page: 737

View: 306

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. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. Quotes This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts. -Sanjay Ranka, University of Florida In my opinion this is currently the best data mining text book on the market. I like the comprehensive coverage which spans all major data mining techniques including classification, clustering, and pattern mining (association rules). -Mohammed Zaki, Rensselaer Polytechnic Institute

INTRODUCTION TO DATA MINING WITH CASE STUDIES

BIBLIOGRAPHY During the last few years a number of good books on data mining have been published. Different books seem to have different focus. For example, the book by Han and Kamber is written from a database perspective while Hand, ...

Author: G. K. GUPTA

Publisher: PHI Learning Pvt. Ltd.

ISBN: 8120350022

Category: Computers

Page: 536

View: 634

The field of data mining provides techniques for automated discovery of valuable information from the accumulated data of computerized operations of enterprises. This book offers a clear and comprehensive introduction to both data mining theory and practice. It is written primarily as a textbook for the students of computer science, management, computer applications, and information technology. The book ensures that the students learn the major data mining techniques even if they do not have a strong mathematical background. The techniques include data pre-processing, association rule mining, supervised classification, cluster analysis, web data mining, search engine query mining, data warehousing and OLAP. To enhance the understanding of the concepts introduced, and to show how the techniques described in the book are used in practice, each chapter is followed by one or two case studies that have been published in scholarly journals. Most case studies deal with real business problems (for example, marketing, e-commerce, CRM). Studying the case studies provides the reader with a greater insight into the data mining techniques. The book also provides many examples, review questions, multiple choice questions, chapter-end exercises and a good list of references and Web resources especially those which are easy to understand and useful for students. A number of class projects have also been included.

Civil Architecture and Environmental Engineering Volume 2

Tan P N, Steinbach M, Kumar V. Introduction to Data Mining: Global Edition, 2/E [M]. Pearson Schweiz Ag. Wang A P, Wang Z F, Tao S G, et al. Common Algorithms of Association Rules Mining in Data Mining [J].

Author: Jimmy C.M. Kao

Publisher: CRC Press

ISBN: 135164937X

Category: Science

Page: 700

View: 407

The 2016 International Conference on Civil, Architecture and Environmental Engineering (ICCAE 2016), November 4-6, 2016, Taipei, Taiwan, is organized by China University of Technology and Taiwan Society of Construction Engineers, aimed to bring together professors, researchers, scholars and industrial pioneers from all over the world. ICCAE 2016 is the premier forum for the presentation and exchange of experience, progress and research results in the field of theoretical and industrial experience. The conference consists of contributions promoting the exchange of ideas between researchers and educators all over the world.

A General Introduction to Data Analytics

Rep., Gartner Inc. 3 Cisco Inc. (2016) White paper: Cisco visual networking index: Global mobile data traffic forecast update, ... 20 Tan, P., Steinbach, M., and Kumar, V. (2014) Introduction to Data Mining, Pearson Education.

Author: João Moreira

Publisher: John Wiley & Sons

ISBN: 1119296269

Category: Mathematics

Page: 352

View: 482

A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniques A unique illustrative example that extends throughout all the chapters Exercises at the end of each chapter and larger projects at the end of each of the text’s two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms.

Multimedia Data Mining and Knowledge Discovery

This volume provides an overview of multimedia data mining and knowledge discovery and discusses the variety of hot topics in multimedia data mining research.

Author: Valery A. Petrushin

Publisher: Springer Science & Business Media

ISBN: 1846287995

Category: Computers

Page: 521

View: 652

This volume provides an overview of multimedia data mining and knowledge discovery and discusses the variety of hot topics in multimedia data mining research. It describes the objectives and current tendencies in multimedia data mining research and their applications. Each part contains an overview of its chapters and leads the reader with a structured approach through the diverse subjects in the field.

Statistical Data Mining and Knowledge Discovery

We also provide quantitative measurement of the error introduced in the recompiled global principal components when the databases are heterogeneous. 323 19.1 Introduction Data mining is a technology that deals with ...

Author: Hamparsum Bozdogan

Publisher: CRC Press

ISBN: 0203497155

Category: Business & Economics

Page: 624

View: 981

Massive data sets pose a great challenge to many cross-disciplinary fields, including statistics. The high dimensionality and different data types and structures have now outstripped the capabilities of traditional statistical, graphical, and data visualization tools. Extracting useful information from such large data sets calls for novel approache

Computational Science ICCS 2009

A Parallel Nonnegative Tensor Factorization Algorithm for Mining Global Climate Data Qiang Zhang1, ... 1 Introduction Data mining techniques are commonly used for the discovery of interesting patterns in earth science data.

Author: Gabrielle Allen

Publisher: Springer Science & Business Media

ISBN: 3642019722

Category: Computers

Page: 920

View: 182

The two-volume set LNCS 5544-5545 constitutes the refereed proceedings of the 9th International Conference on Computational Science, ICCS 2009, held in Baton Rouge, LA, USA in May 2008. The 60 revised papers of the main conference track presented together with the abstracts of 5 keynote talks and the 138 revised papers from 13 workshops were carefully reviewed and selected for inclusion in the three volumes. The general main track of ICSS 2009 was organized in about 20 parallel sessions addressing the following topics: e-Science Applications and Systems, Scheduling, Software Services and Tools, New Hardware and Its Applications, Computer Networks, Simulation of Complex Systems, Image Processing, Optimization Techniques, and Numerical Methods.

Data Mining and Predictive Analytics

INTRODUCTION. TO. DATA. MINING. AND. PREDICTIVE. ANALYTICS. 1.1 WHAT IS DATA MINING? WHAT IS PREDICTIVE ANALYTICS? ... 2015 John Wiley & Sons, Inc. Published 2015 by John Wiley & Sons, Inc. 3 The McKinsey Global Institute (MGI) reports3 ...

Author: Daniel T. Larose

Publisher: John Wiley & Sons

ISBN: 1118868706

Category: Computers

Page: 824

View: 307

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.