Data Modeling Fundamentals: A Practical Guide for IT Professionals

Data Modeling Fundamentals: A Practical Guide for IT Professionals
EBook | PDF | 12.1 MB

The purpose of this book is to provide a practical approach for IT professionals to acquire the necessary knowledge and expertise in data modeling to function effectively.

It begins with an overview of basic data modeling concepts, introduces the methods and techniques, provides a comprehensive case study to present the details of the data model components, covers the implementation of the data model with emphasis on quality components, and concludes with a presentation of a realistic approach to data modeling. It clearly describes how a generic data model is created to represent truly the enterprise information requirements.

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EBook Quality Measures in Data Mining

Quality Measures in Data Mining | PDF | EBook

Data mining analyzes large amounts of data to discover knowledge relevant to decision making. Typically, numerous pieces of knowledge are extracted by a data mining system and presented to a human user, who may be a decision-maker or a data-analyst. The user is confronted with the task of selecting the pieces of knowledge that are of the highest quality or interest according to his or her preferences. Since this selection is sometimes a daunting task, designing quality and interestingness measures has become an important challenge for data mining researchers in the last decade.

This volume presents the state of the art concerning quality and interestingness measures for data mining. The book summarizes recent developments and presents original research on this topic. The chapters include surveys, comparative studies of existing measures, proposals of new measures, simulations, and case studies. Both theoretical and applied chapters are included. Papers for this book were selected and reviewed for correctness and completeness by an international review committee.

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Ebook Advanced Data Mining Techniques

Advanced Data Mining Techniques | EBook | Pdf

This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focusses on business applications of data mining. Methods are presented with simple examples, applications are reviewed, and relativ advantages are evaluated.

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The Handbook of Data Mining

The Handbook of Data Mining | Handbook

Created with the input of a distinguished International Board of the foremost authorities in data mining from academia and industry, The Handbook of Data Mining presents comprehensive coverage of data mining concepts and techniques. Algorithms, methodologies, management issues, and tools are all illustrated through engaging examples and real-world applications to ease understanding of the materials.

This book is organized into three parts. Part I presents various data mining methodologies, concepts, and available software tools for each methodology. Part II addresses various issues typically faced in the management of data mining projects and tips on how to maximize outcome utility. Part III features numerous real-world applications of these techniques in a variety of areas, including human performance, geospatial, bioinformatics, on- and off-line customer transaction activity, security-related computer audits, network traffic, text and image, and manufacturing quality.

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EBook Lecture Notes in Data Mining

The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field. This book is a series of seventeen edited "student-authored lectures" which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight. The initial chapters lay a framework of data mining techniques by explaining some of the basics such as applications of Bayes Theorem, similarity measures, and decision trees. Before focusing on the pillars of classification, clustering, and association rules, this book also considers alternative candidates such as point estimation and genetic algorithms. The book's discussion of classification includes an introduction to decision tree algorithms, rule-based algorithms (a popular alternative to decision trees) and distance-based algorithms. Five of the lecture-chapters are devoted to the concept of clustering or unsupervised classification. The functionality of hierarchical and partitional clustering algorithms is also covered as well as the efficient and scalable clustering algorithms used in large databases. The concept of association rules in terms of basic algorithms, parallel and distributive algorithms and advanced measures that help determine the value of association rules are discussed. The final chapter discusses algorithms for spatial data mining.

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Lecture Notes in Data Mining

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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques

This book will give the reader a perspective into the core theory and practice of data mining and knowledge discovery (DM and KD). Its chapters combine many theoretical foundations for various DM and KD methods, and they present a rich array of examples – many of which are drawn from real-life applications. Most of the theoretical developments discussed are accompanied by an extensive empirical analysis, which should give the reader both a deep theoretical and practical insight into the subjects covered.

The book presents the combined research experiences of its 40 authors gathered during a long search in gleaning new knowledge from data. The last page of each chapter has a brief biographical statement of its contributors, who are world-renowned experts.

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Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management

In order to find new ways to improve customer sales and support, and as well as manage risk, business managers must be able to mine company databases. This book provides a step-by-step guide to creating and implementing models of the most commonly asked data mining questions. Readers will learn how to prepare data to mine, and develop accurate data mining questions. The author, who has over ten years of data mining experience, also provides actual tested models of specific data mining questions for marketing, sales, customer service and retention, and risk management. A CD-ROM, sold separately, provides these models for reader use.

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Data Mining in Grid Computing Environments

Based around eleven international real life case studies and including contributions from leading experts in the field this groundbreaking book explores the need for the grid-enabling of data mining applications and provides a comprehensive study of the technology, techniques and management skills necessary to create them. This book provides a simultaneous design blueprint, user guide, and research agenda for current and future developments and will appeal to a broad audience; from developers and users of data mining and grid technology, to advanced undergraduate and postgraduate students interested in this field.

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Data Mining in Grid Computing Environments

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EBook Data Mining Using SAS Enterprise Miner

Data Mining Using SAS® Enterprise Miner introduces the reader to a wide variety of data mining techniques in SAS® Enterprise Miner. This first-of-a-kind book explains the purpose of -- and reasoning behind -- every node that is a part of Enterprise Miner with regard to SEMMA design and data mining analysis. Each chapter starts with a short introduction to the assortment of statistics that are generated from the various Enterprise Miner nodes, followed by detailed explanations of configuration settings that are located within each node. The end result of the author’s meticulous presentation is a well crafted study guide on the various methods that one employs to both randomly sample and partition data within the process flow of SAS® Enterprise Miner.

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Data Mining Using SAS Enterprise Miner

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Mathematical Methods for Knowledge Discovery and Data Mining

The field of data mining has seen a demand in recent years for the development of ideas and results in an integrated structure. Mathematical Methods for Knowledge Discovery & Data Mining focuses on the mathematical models and methods that support most data mining applications and solution techniques, covering such topics as association rules; Bayesian methods; data visualization; kernel methods; neural networks; text, speech, and image recognition; and many others. This Premier Reference Source is an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance and insurance, manufacturing, marketing, performance measurement, and telecommunications.

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Statistical Data Mining & Knowledge Discovery

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 approaches that meld concepts, tools, and techniques from diverse areas, such as computer science, statistics, artificial intelligence, and financial engineering.Statistical Data Mining and Knowledge Discovery brings together a stellar panel of experts to discuss and disseminate recent developments in data analysis techniques for data mining and knowledge extraction. This carefully edited collection provides a practical, multidisciplinary perspective on using statistical techniques in areas such as market segmentation, customer profiling, image and speech analysis, and fraud detection. The chapter authors, who include such luminaries as Arnold Zellner, S. James Press, Stephen Fienberg, and Edward J. Wegman, present novel approaches and innovative models and relate their experiences in using data mining techniques in a wide range of applications.

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Statistical Data Mining & Knowledge Discovery

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Data Mining: A Heuristic Approach

Real life problems are known to be messy, dynamic and multi-objective, and involve high levels of uncertainty and constraints. Because traditional problem-solving methods are no longer capable of handling this level of complexity, heuristic search methods have attracted increasing attention in recent years for solving such problems. Inspired by nature, biology, statistical mechanics, physics and neuroscience, heuristics techniques are used to solve many problems where traditional methods have failed. Data Mining: A Heuristic Approach will be a repository for the applications of these techniques in the area of data mining.

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Principles of Data Mining

Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas.

This book explains and explores the principal techniques of Data Mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples and explanations of the algorithms given. This should prove of value to readers of all kinds, from those whose only use of data mining techniques will be via commercial packages right through to academic researchers.

This book aims to help the general reader develop the necessary understanding to use commercial data mining packages discriminatingly, as well as enabling the advanced reader 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.

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Mastering The SAP Business : Information Warehouse

"This book is the definitive guide for SAP NetWeaver BI professionals. Based on their extraordinary expertise with the product, the authors provide deep insights about key innovations in the areas of user experience, query performance, integrated planning, and enterprise-wide data warehousing."
—Stefan Sigg, Vice President, SAP NetWeaver Business Intelligence

The long-anticipated publication of this second edition reflects the growing success of SAP NetWeaver as well as the various Business Intelligence (BI) capabilities that are embedded with SAP BW version 7.0. Written by SAP insiders, this comprehensive guide takes into account the ever-changing features, functionality, and toolsets of SAP NetWeaver to bring you the most updated information on how to use SAP BW to design, build, deploy, populate, access, analyze, present, and administer data. You'll discover the options that are available in SAP NetWeaver and uncover a new means to improve business performance.

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Mastering Data Mining: The Art and Science of Customer Relationship Management

Berry and Linoff lead the reader down an enlightened path of best practices." -Dr. Jim Goodnight, President and Cofounder, SAS Institute Inc.

"This is a great book, and it will be in my stack of four or five essential resources for my professional work." -Ralph Kimball, Author of The Data Warehouse Lifecycle Toolkit

Mastering Data Mining

In this follow-up to their successful first book, Data Mining Techniques, Michael J. A. Berry and Gordon S. Linoff offer a case study-based guide to best practices in commercial data mining. Their first book acquainted you with the new generation of data mining tools and techniques and showed you how to use them to make better business decisions. Mastering Data Mining shifts the focus from understanding data mining techniques to achieving business results, placing particular emphasis on customer relationship management.

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Data Mining Techniques : For Marketing, Sales, and Customer Relationship Management, 2nd Edition

• Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems
• Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support
• The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining
• More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining
• Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis

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Data Mining Techniques : For Marketing, Sales, and Customer Relationship Management, 2nd Edition

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Data Mining: Practical Machine Learning Tools and Techniques, Second Edition

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.

The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.

• Algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods
• Performance improvement techniques that work by transforming the input or output
• Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface

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Data Mining Methods and Models

Data Mining Methods and Models provides:

1. The latest techniques for uncovering hidden nuggets of information
2. The insight into how the data mining algorithms actually work
3. The hands-on experience of performing data mining on large data sets

Data Mining Methods and Models:

• Applies a "white box" methodology, emphasizing an understanding of the model structures underlying the softwareWalks the reader through the various algorithms and provides examples of the operation of the algorithms on actual large data sets, including a detailed case study, "Modeling Response to Direct-Mail Marketing"
• Tests the reader's level of understanding of the concepts and methodologies, with over 110 chapter exercises
• Demonstrates the Clementine data mining software suite, WEKA open source data mining software, SPSS statistical software, and Minitab statistical software
• Includes a companion Web site, www.dataminingconsultant.com, where the data sets used in the book may be downloaded, along with a comprehensive set of data mining resources. Faculty adopters of the book have access to an array of helpful resources, including solutions to all exercises, a PowerPoint(r) presentation of each chapter, sample data mining course projects and accompanying data sets, and multiple-choice chapter quizzes.

With its emphasis on learning by doing, this is an excellent textbook for students in business, computer science, and statistics, as well as a problem-solving reference for data analysts and professionals in the field.

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Discovering Knowledge in Data : An Introduction to Data Mining

Learn Data Mining by doing data mining
Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.
Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include:

• Data preprocessing and classification
• Exploratory analysis
• Decision trees
• Neural and Kohonen networks
• Hierarchical and k-means clustering
• Association rules
• Model evaluation techniques

Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge.

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Data Mining: Concepts and Techniques, Second Edition

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.

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