Data Mining for Business Analytics

Data Mining for Business Analytics
Author: Galit Shmueli,Peter C. Bruce,Peter Gedeck,Nitin R. Patel
Publsiher: John Wiley & Sons
Total Pages: 608
Release: 2019-11-05
ISBN 10: 1119549841
ISBN 13: 9781119549840
Language: EN, FR, DE, ES & NL

Data Mining for Business Analytics Book Review:

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

Data Mining Concepts and Techniques

Data Mining  Concepts and Techniques
Author: Jiawei Han,Jian Pei,Micheline Kamber
Publsiher: Elsevier
Total Pages: 744
Release: 2011-06-09
ISBN 10: 9780123814807
ISBN 13: 0123814804
Language: EN, FR, DE, ES & NL

Data Mining Concepts and Techniques Book Review:

Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Data Mining

Data Mining
Author: Florin Gorunescu
Publsiher: Springer Science & Business Media
Total Pages: 360
Release: 2011-03-10
ISBN 10: 9783642197215
ISBN 13: 3642197213
Language: EN, FR, DE, ES & NL

Data Mining Book Review:

The knowledge discovery process is as old as Homo sapiens. Until some time ago this process was solely based on the ‘natural personal' computer provided by Mother Nature. Fortunately, in recent decades the problem has begun to be solved based on the development of the Data mining technology, aided by the huge computational power of the 'artificial' computers. Digging intelligently in different large databases, data mining aims to extract implicit, previously unknown and potentially useful information from data, since “knowledge is power”. The goal of this book is to provide, in a friendly way, both theoretical concepts and, especially, practical techniques of this exciting field, ready to be applied in real-world situations. Accordingly, it is meant for all those who wish to learn how to explore and analysis of large quantities of data in order to discover the hidden nugget of information.

Data Preparation for Data Mining

Data Preparation for Data Mining
Author: Dorian Pyle
Publsiher: Morgan Kaufmann
Total Pages: 540
Release: 1999-04-05
ISBN 10: 9781558605299
ISBN 13: 1558605290
Language: EN, FR, DE, ES & NL

Data Preparation for Data Mining Book Review:

Data Preparation for Data Mining addresses an issue unfortunately ignored by most authorities on data mining: data preparation. Thanks largely to its perceived difficulty, data preparation has traditionally taken a backseat to the more alluring question of how best to extract meaningful knowledge. But without adequate preparation of your data, the return on the resources invested in mining is certain to be disappointing. Dorian Pyle corrects this imbalance. A twenty-five-year veteran of what has become the data mining industry, Pyle shares his own successful data preparation methodology, offering both a conceptual overview for managers and complete technical details for IT professionals. Apply his techniques and watch your mining efforts pay off-in the form of improved performance, reduced distortion, and more valuable results. On the enclosed CD-ROM, you'll find a suite of programs as C source code and compiled into a command-line-driven toolkit. This code illustrates how the author's techniques can be applied to arrive at an automated preparation solution that works for you. Also included are demonstration versions of three commercial products that help with data preparation, along with sample data with which you can practice and experiment. * Offers in-depth coverage of an essential but largely ignored subject. * Goes far beyond theory, leading you-step by step-through the author's own data preparation techniques. * Provides practical illustrations of the author's methodology using realistic sample data sets. * Includes algorithms you can apply directly to your own project, along with instructions for understanding when automation is possible and when greater intervention is required. * Explains how to identify and correct data problems that may be present in your application. * Prepares miners, helping them head into preparation with a better understanding of data sets and their limitations.

Computational Intelligence in Data Mining

Computational Intelligence in Data Mining
Author: Giacomo Della Riccia,Rudolf Kruse,Hans-J. Lenz,Hans-Joachim Lenz
Publsiher: Springer Science & Business Media
Total Pages: 166
Release: 2000-05-31
ISBN 10: 9783211833261
ISBN 13: 3211833269
Language: EN, FR, DE, ES & NL

Computational Intelligence in Data Mining Book Review:

The book aims to merge Computational Intelligence with Data Mining, which are both hot topics of current research and industrial development, Computational Intelligence, incorporates techniques like data fusion, uncertain reasoning, heuristic search, learning, and soft computing. Data Mining focuses on unscrambling unknown patterns or structures in very large data sets. Under the headline "Discovering Structures in Large Databases” the book starts with a unified view on 'Data Mining and Statistics – A System Point of View'. Two special techniques follow: 'Subgroup Mining', and 'Data Mining with Possibilistic Graphical Models'. "Data Fusion and Possibilistic or Fuzzy Data Analysis” is the next area of interest. An overview of possibilistic logic, nonmonotonic reasoning and data fusion is given, the coherence problem between data and non-linear fuzzy models is tackled, and outlier detection based on learning of fuzzy models is studied. In the domain of "Classification and Decomposition” adaptive clustering and visualisation of high dimensional data sets is introduced. Finally, in the section "Learning and Data Fusion” learning of special multi-agents of virtual soccer is considered. The last topic is on data fusion based on stochastic models.

Predictive Data Mining

Predictive Data Mining
Author: Sholom M. Weiss,Nitin Indurkhya
Publsiher: Morgan Kaufmann
Total Pages: 228
Release: 1998
ISBN 10: 9781558604032
ISBN 13: 1558604030
Language: EN, FR, DE, ES & NL

Predictive Data Mining Book Review:

This book is the first technical guide to provide a complete, generalized road map for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses.

Data Mining and Machine Learning

Data Mining and Machine Learning
Author: Mohammed J. Zaki,Wagner Meira, Jr
Publsiher: Cambridge University Press
Total Pages: 775
Release: 2020-01-31
ISBN 10: 1108473989
ISBN 13: 9781108473989
Language: EN, FR, DE, ES & NL

Data Mining and Machine Learning Book Review:

New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.

Data Mining

Data Mining
Author: Charu C. Aggarwal
Publsiher: Springer
Total Pages: 734
Release: 2015-04-13
ISBN 10: 3319141422
ISBN 13: 9783319141428
Language: EN, FR, DE, ES & NL

Data Mining Book Review:

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 domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago

Data Mining Techniques

Data Mining Techniques
Author: Michael J. A. Berry,Gordon S. Linoff
Publsiher: John Wiley & Sons
Total Pages: 643
Release: 2004-04-09
ISBN 10: 0471470643
ISBN 13: 9780471470649
Language: EN, FR, DE, ES & NL

Data Mining Techniques Book Review:

Many companies have invested in building large databases and data warehouses capable of storing vast amounts of information. This book offers business, sales and marketing managers a practical guide to accessing such information.

Applied Data Mining

Applied Data Mining
Author: Guandong Xu,Yu Zong,Zhenglu Yang
Publsiher: CRC Press
Total Pages: 284
Release: 2013-06-17
ISBN 10: 1466585846
ISBN 13: 9781466585843
Language: EN, FR, DE, ES & NL

Applied Data Mining Book Review:

Data mining has witnessed substantial advances in recent decades. New research questions and practical challenges have arisen from emerging areas and applications within the various fields closely related to human daily life, e.g. social media and social networking. This book aims to bridge the gap between traditional data mining and the latest advances in newly emerging information services. It explores the extension of well-studied algorithms and approaches into these new research arenas.

Contrast Data Mining

Contrast Data Mining
Author: Guozhu Dong,James Bailey
Publsiher: CRC Press
Total Pages: 434
Release: 2012-09-07
ISBN 10: 1439854327
ISBN 13: 9781439854327
Language: EN, FR, DE, ES & NL

Contrast Data Mining Book Review:

A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life Problems Contrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and other fields. The book not only presents concepts and techniques for contrast data mining, but also explores the use of contrast mining to solve challenging problems in various scientific, medical, and business domains. Learn from Real Case Studies of Contrast Mining Applications In this volume, researchers from around the world specializing in architecture engineering, bioinformatics, computer science, medicine, and systems engineering focus on the mining and use of contrast patterns. They demonstrate many useful and powerful capabilities of a variety of contrast mining techniques and algorithms, including tree-based structures, zero-suppressed binary decision diagrams, data cube representations, and clustering algorithms. They also examine how contrast mining is used in leukemia characterization, discriminative gene transfer and microarray analysis, computational toxicology, spatial and image data classification, voting analysis, heart disease prediction, crime analysis, understanding customer behavior, genetic algorithms, and network security.

Practical Applications of Data Mining

Practical Applications of Data Mining
Author: Sang C. Suh
Publsiher: Jones & Bartlett Publishers
Total Pages: 414
Release: 2012
ISBN 10: 0763785873
ISBN 13: 9780763785871
Language: EN, FR, DE, ES & NL

Practical Applications of Data Mining Book Review:

Various topics of data mining techniques are identified and described throughout, including clustering, association rules, rough set theory, probability theory, neural networks, classification, and fuzzy logic. Each of these techniques is explored with a theoretical introduction and its effectiveness is demonstrated with various chapter examples.

Data Mining for the Social Sciences

Data Mining for the Social Sciences
Author: Paul Attewell,David Monaghan,Darren Kwong
Publsiher: Univ of California Press
Total Pages: 252
Release: 2015-05
ISBN 10: 0520280989
ISBN 13: 9780520280984
Language: EN, FR, DE, ES & NL

Data Mining for the Social Sciences Book Review:

"We live, today, in world of big data. The amount of information collected on human behavior every day is staggering, and exponentially greater than at any time in the past. At the same time, we are inundated by stories of powerful algorithms capable of churning through this sea of data and uncovering patterns. These techniques go by many names - data mining, predictive analytics, machine learning - and they are being used by governments as they spy on citizens and by huge corporations are they fine-tune their advertising strategies. And yet social scientists continue mainly to employ a set of analytical tools developed in an earlier era when data was sparse and difficult to come by. In this timely book, Paul Attewell and David Monaghan provide a simple and accessible introduction to Data Mining geared towards social scientists. They discuss how the data mining approach differs substantially, and in some ways radically, from that of conventional statistical modeling familiar to most social scientists. They demystify data mining, describing the diverse set of techniques that the term covers and discussing the strengths and weaknesses of the various approaches. Finally they give practical demonstrations of how to carry out analyses using data mining tools in a number of statistical software packages. It is the hope of the authors that this book will empower social scientists to consider incorporating data mining methodologies in their analytical toolkits"--Provided by publisher.

Introduction to Data Mining and Its Applications

Introduction to Data Mining and Its Applications
Author: S. Sumathi,S.N. Sivanandam
Publsiher: Springer Science & Business Media
Total Pages: 828
Release: 2006-09-26
ISBN 10: 3540343504
ISBN 13: 9783540343509
Language: EN, FR, DE, ES & NL

Introduction to Data Mining and Its Applications Book Review:

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.

Data Mining with Rattle and R

Data Mining with Rattle and R
Author: Graham Williams
Publsiher: Springer
Total Pages: 374
Release: 2011-02-25
ISBN 10: 9781441998897
ISBN 13: 1441998896
Language: EN, FR, DE, ES & NL

Data Mining with Rattle and R Book Review:

Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.

Mining of Massive Datasets

Mining of Massive Datasets
Author: Jure Leskovec,Anand Rajaraman,Jeffrey David Ullman
Publsiher: Cambridge University Press
Total Pages: 476
Release: 2014-11-13
ISBN 10: 1107077230
ISBN 13: 9781107077232
Language: EN, FR, DE, ES & NL

Mining of Massive Datasets Book Review:

Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.

Data Mining

Data Mining
Author: Nong Ye
Publsiher: CRC Press
Total Pages: 349
Release: 2013-07-26
ISBN 10: 1439808384
ISBN 13: 9781439808382
Language: EN, FR, DE, ES & NL

Data Mining Book Review:

New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms. The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures. The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them.

Introduction to Data Mining

Introduction to Data Mining
Author: Pang-Ning Tan,Michael Steinbach,Anuj Karpatne,Vipin Kumar
Publsiher: Addison-Wesley
Total Pages: 839
Release: 2019
ISBN 10: 9780133128901
ISBN 13: 0133128903
Language: EN, FR, DE, ES & NL

Introduction to Data Mining Book Review:

Introduction to Data Mining, Second Edition, is intended for use in the Data Mining course. It is also suitable for individuals seeking an introduction to data mining. The text assumes only a modest statistics or mathematics background, and no database knowledge is needed. 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. Teaching and Learning Experience This program will provide a better teaching and learning experience-for you and your students. It will help: Present Fundamental Concepts and Algorithms: Written for the beginner, this text provides both theoretical and practical coverage of all data mining topics. Support Learning: Instructor resources include solutions for exercises and a complete set of lecture slides.

Principles of Data Mining

Principles of Data Mining
Author: Max Bramer
Publsiher: Springer
Total Pages: 571
Release: 2020-05-21
ISBN 10: 9781447174929
ISBN 13: 1447174925
Language: EN, FR, DE, ES & NL

Principles of Data Mining Book Review:

This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self-study, it aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. Principles of Data Mining includes descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift. The expanded fourth edition gives a detailed description of a feed-forward neural network with backpropagation and shows how it can be used for classification.

Educational Data Mining

Educational Data Mining
Author: Alejandro Peña-Ayala
Publsiher: Springer
Total Pages: 468
Release: 2013-11-08
ISBN 10: 3319027387
ISBN 13: 9783319027388
Language: EN, FR, DE, ES & NL

Educational Data Mining Book Review:

This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research. After a rigorous revision process sixteen manuscripts were accepted and organized into four parts as follows: · Profile: The first part embraces three chapters oriented to: 1) describe the nature of educational data mining (EDM); 2) describe how to pre-process raw data to facilitate data mining (DM); 3) explain how EDM supports government policies to enhance education. · Student modeling: The second part contains five chapters concerned with: 4) explore the factors having an impact on the student's academic success; 5) detect student's personality and behaviors in an educational game; 6) predict students performance to adjust content and strategies; 7) identify students who will most benefit from tutor support; 8) hypothesize the student answer correctness based on eye metrics and mouse click. · Assessment: The third part has four chapters related to: 9) analyze the coherence of student research proposals; 10) automatically generate tests based on competences; 11) recognize students activities and visualize these activities for being presented to teachers; 12) find the most dependent test items in students response data. · Trends: The fourth part encompasses four chapters about how to: 13) mine text for assessing students productions and supporting teachers; 14) scan student comments by statistical and text mining techniques; 15) sketch a social network analysis (SNA) to discover student behavior profiles and depict models about their collaboration; 16) evaluate the structure of interactions between the students in social networks. This volume will be a source of interest to researchers, practitioners, professors, and postgraduate students aimed at updating their knowledge and find targets for future work in the field of educational data mining.