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Data Mining for the Masses, Third Edition

Author : Matthew North

Publisher :

Page : 310 pages

File Size : 35,7 MB

Release : 2018-09-05

Category :

ISBN : 9781727102475

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Book Description

Some say we live in the Information Age; others, the Social Age; and still others, the Big Data Age. Regardless of what name we give it, we live in an age that generates monumental amounts of data-in all different kinds of formats. In business, and in our personal lives, we use smartphones and tablets, web sites and watches; with apps and interfaces to shop, learn, entertain and inform. Businesses increasingly use technology to interact with consumers to provide marketing, customer service, product information and more. All of this technological activity generates data, and we're increasingly good at gathering, storing and analyzing it.Data mining can help to identify interesting patterns and messages that exist in data, often hidden beneath the surface. In this modern age of information systems, it is easier than ever before to extract meaning from data. From classification to prediction, data mining can help.In Data Mining for the Masses, Third Edition, professor Matt North-a former risk analyst and software engineer at eBay-uses simple examples and clear explanations with free, powerful software tools to teach you the basics of data mining. In this Third Edition, implementations of these examples are offered in current versions of the RapidMiner software, and in the increasingly popular R Statistical Package.You've got more data than ever before and you know it's got value, if only you can figure out how to get to it. This book can show you how. Let's start digging!



Data Mining Techniques

Author : Michael J. A. Berry

Publisher : John Wiley & Sons

Page : 671 pages

File Size : 29,9 MB

Release : 2004-04-09

Category : Business & Economics

ISBN : 0471470643

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Book Description

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.



Mining of Massive Datasets

Author : Jure Leskovec

Publisher : Cambridge University Press

Page : 480 pages

File Size : 23,99 MB

Release : 2014-11-13

Category : Computers

ISBN : 1107077230

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Book Description

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



Principles of Data Mining

Author : Max Bramer

Publisher : Springer

Page : 530 pages

File Size : 24,10 MB

Release : 2016-11-09

Category : Computers

ISBN : 1447173074

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Book Description

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, this book 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. This expanded third edition includes detailed 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.



Principles of Data Mining

Author : David J. Hand

Publisher : MIT Press

Page : 594 pages

File Size : 18,47 MB

Release : 2001-08-17

Category : Computers

ISBN : 9780262082907

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Book Description

The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.



Predictive Analytics and Data Mining

Author : Vijay Kotu

Publisher : Morgan Kaufmann

Page : 447 pages

File Size : 43,7 MB

Release : 2014-11-27

Category : Computers

ISBN : 0128016507

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Book Description

Put Predictive Analytics into ActionLearn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining.You’ll be able to:1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process.2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases.3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com Demystifies data mining concepts with easy to understand language Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis Explains the process of using open source RapidMiner tools Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics Includes practical use cases and examples



Data Mining and Predictive Analytics

Author : Daniel T. Larose

Publisher : John Wiley & Sons

Page : 827 pages

File Size : 50,90 MB

Release : 2015-02-19

Category : Computers

ISBN : 1118868676

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Book Description

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.



Data Mining: Concepts and Techniques

Author : Jiawei Han

Publisher : Elsevier

Page : 740 pages

File Size : 43,87 MB

Release : 2011-06-09

Category : Computers

ISBN : 0123814804

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Book Description

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



Descriptive Data Mining

Author : David L. Olson

Publisher : Springer

Page : 139 pages

File Size : 50,44 MB

Release : 2019-05-06

Category : Business & Economics

ISBN : 9811371814

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Book Description

This book provides an overview of data mining methods demonstrated by software. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Diagnostic analytics can apply analysis to sensor input to direct control systems automatically. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on descriptive analytics. The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic software support to data visualization. Chapter 3 covers fundamentals of market basket analysis, and Chapter 4 provides demonstration of RFM modeling, a basic marketing data mining tool. Chapter 5 demonstrates association rule mining. Chapter 6 is a more in-depth coverage of cluster analysis. Chapter 7 discusses link analysis. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.



Rock Mechanics

Author : B. H. G. Brady

Publisher : Springer Science & Business Media

Page : 542 pages

File Size : 26,77 MB

Release : 2012-12-06

Category : Science

ISBN : 9401165017

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Book Description

Rock mechanics is a field of applied science which has become recognised as a coherent engineering discipline within the last two decades. It consists of a body of knowledge of the mechanical properties of rock, various techniques for the analysis of rock stress under some imposed perturbation, a set of established principles expressing rock mass response to load, and a logical methodology for applying these notions and techniques to real physical prob lems. Some of the areas where application of rock mechanics concepts have been demonstrated to be of industrial value include surface and subsurface construction, mining and other methods of mineral recovery, geothermal energy recovery and subsurface hazardous waste isolation. In many cases, the pressures of industrial demand for rigour and precision in project or process design have led to rapid evolution of the engineering discipline, and general improvement in its basis in both the geosciences and engineering mechanics. An intellectual commitment in some outstanding research centres to the proper development of rock mechanics has now resulted in a capacity for engineering design in rock not conceivable two decades ago. Mining engineering is an obvious candidate for application of rock mechanics principles in the design of excavations generated by mineral extrac tion. A primary concern in mining operations, either on surface or underground, is loosely termed 'ground control', i. e.