Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems)

Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems)
Morgan Kaufmann | 1999-03-15 | ISBN: 1558605290 | 540 pages | PDF | 5 MB

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.

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Applied Data Mining for Business and Industry,2 ed

Paolo Giudici, Silvia Figini, "Applied Data Mining for Business and Industry,2 ed"
Wiley | 2009 | ISBN: 0470058870 | 258 pages | PDF | 1,6 MB

The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book pres an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications.

Introduces data mining methods and applications.
Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods.
Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining.
Features detailed case studies based on applied projects within industry.
Incorporates discussion of data mining software, with case studies analysed using R.
Is accessible to anyone with a basic knowledge of statistics or data analysis.
Includes an extensive bibliography and pointers to further reading within the text.

Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will pre guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance.

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Data Mining with Decision Trees: Theroy and Applications

Lior Rokach, Oded Maimon, "Data Mining with Decision Trees: Theroy and Applications"
World Scientific Publishing Company | 2008 | ISBN: 9812771719 | 244 pages | PDF | 1,4 MB

This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique. Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition.This book invites readers to explore the many benefits in data mining that decision trees offer: self-explanatory and easy to follow when compacted; able to handle a variety of input data: nominal, numeric and textual; able to process datasets that may have errors or missing values; high predictive performance for a relatively small computational effort; available in many data mining packages over a variety of platforms; and, useful for various tasks, such as classification, regression, clustering and feature selection.

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The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning,Conforming,and Delivering Data

The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning,Conforming,and Delivering Data
Publisher: Wiley | ISBN: 0764567578 | edition 2004 | PDF | 528 pages | 12,2 mb

* Cowritten by Ralph Kimball, the world's leading data warehousing authority, whose previous books have sold more than 150,000 copies
* Delivers real-world solutions for the most time- and labor-intensive portion of data warehousing-data staging, or the extract, transform, load (ETL) process
* Delineates best practices for extracting data from scattered sources, removing redundant and inaccurate data, transforming the remaining data into correctly formatted data structures, and then loading the end product into the data warehouse
* Offers proven time-saving ETL techniques, comprehensive guidance on building dimensional structures, and crucial advice on ensuring data quality

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Advanced Data Mining Techniques

Advanced Data Mining Techniques
Springer | 2008 | English | ISBN: 3540769161 | PDF | 180 pages | 1.07MB

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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Principles of Data Mining (Adaptive Computation and Machine Learning)

Principles of Data Mining (Adaptive Computation and Machine Learning)
Publisher: The MIT Press | ISBN: 026208290X | edition 2001 | PDF | 578 pages | 30,64 mb

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.

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Professional SQL Server 2000 Data Warehousing with Analysis Services

Chris Graves,Mark Scott,Mike Benkovich,Paul Turley,Robert Skoglund,Robin Dewson,Sakhr Youness,Denny Lee,Sam Ferguson,Tony Bain,Terrence Joubert, "Professional SQL Server 2000 Data Warehousing with Analysis Services"
Publisher: Peer Information Inc.; 1st edition | 2001 | 701 Pages | ISBN: 1861005407 | PDF | 14 MB

Data warehouses have evolved to cope with the huge volumes of data flowing through the workplace by separating the data used for reporting and decision making from the operational systems. The purpose of the data warehouse is simply to store the raw data, and in combination with Microsoft SQL Server 2000 Analysis Services, this data can be transformed into accessible information that reflects the real factors affecting the enterprise.

In this book, we introduce the key concepts of data warehousing, OLAP, and data mining. In addition to coverage of Data Transformation Services (DTS) and MDX, this book also demonstrates how to develop an Analysis Services client application, and how to secure and optimize your data warehouse. There is also an in-depth discussion of the exciting new topic of Web Housing.

By reading this book, you will learn how best to employ data warehousing and OLAP in your business, and how to leverage it to provide your organization with improved revenue and profitability.

This book covers:

Understanding Analysis Services Architecture
Designing Data Warehouses and Data Marts
Using Data Transformation Services (DTS) in Data Warehousing
Techniques for Data Mining and Analysis
Building OLAP cubes with Analysis Manager, and programmatically through DSO
Securing, Administrating and Optimizing a Data Warehouse and OLAP system
Using Multidimensional Expressions (MDX) to query OLAP cubes
Building OLAP client applications with Visual Basic and ASP
English Query, PivotTable Service

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Data Warehousing Design and Advanced Engineering Applications: Methods for Complex Construction

Ladjel Bellatreche, "Data Warehousing Design and Advanced Engineering Applications: Methods for Complex Construction"
Information Science Reference | 2009 | ISBN: 1605667560 | 362 pages | PDF | 10 MB

Data warehousing and online analysis technologies have shown their effectiveness in managing and analyzing a large amount of disparate data, attracting much attention from numerous research communities. Data Warehousing Design and Advanced Engineering Applications: Methods for Complex Construction covers the complete process of analyzing data to extract, transform, load, and manage the essential components of a data warehousing system. A defining collection of field discoveries, this advanced title provides significant industry solutions for those involved in this distinct research community.

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Strategic Data Warehousing: Achieving Alignment with Business

Strategic Data Warehousing: Achieving Alignment with Business By Neera Bhansali
Publisher: AUERBACH 2009-07-29 | 224 Pages | ISBN: 1420083945 | PDF | 2.2 MB

Strategic Data Warehousing: Achieving Alignment with Business provides an integrated approach to achieving successful and sustainable alignment of data warehouses and business goals. It details the roles and responsibilities of the data warehouse and business managers in achieving strategic alignment, technical integration, and improved flexibility. Complete with case studies depicting real-world scenarios, the text examines the organizational, user, data, and technological factors proven to promote successful data warehousing, provides actionable solutions for achieving strategic alignment, and includes a model that readers can apply in aligning their own data warehouse needs and business goals.

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Data Warehousing: Architecture and Implementation

Data Warehousing: Architecture and Implementation
Prentice Hall PTR | January 9, 1999 | ISBN-10: 0130809020 | 360 pages | PDF | 2.7 mb

This book is intended for Information Technology (IT) professionals who have been hearing about or have been tasked to evaluate, learn or implement data warehousing technologies. Far from being just a passing fad, data warehousing technology has grown much in scale and reputation in the past few years, as evidenced by the increasing number of products, vendors, organizations, and yes, even books, devoted to the subject. Enterprises that have successfully implemented data warehouses find it strategic and often wonder how they ever managed to survive without it in the past. As early as 1995, a Gartner Group survey of Fortune 500 IT managers found that 90 percent of all organizations had planned to implement data warehouses by 1998. Virtually all Top-100 US banks will actively use a data warehouse-based profitability application by 1998. Nearly 30 percent of companies that actively pursue this technology have created a permanent or semipermanent unit to plan, create, maintain, promote, and support the data warehouse. If you are an IT professional who has been tasked with planning, managing, designing, implementing, supporting, or maintaining your organization's data warehouse, then this book is intended for you. The first section introduces the Enterprise Architecture and Data Warehouse concepts, the basis of the reasons for writing this book. The second section of this book focuses on three of the key People in any data warehousing initiative: the Project Sponsor, the CIO, and the Project Manager. This section is devoted to addressing the primary concerns of these individuals.

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Mastering Data Warehouse Aggregates: Solutions for Star Schema Performance

Christopher Adamson, Mastering Data Warehouse Aggregates: Solutions for Star Schema Performance
Wiley | ISBN: 0471777099 | Year 2006 | 345 Pages | PDF | 6.08 MB

The first book to offer in-depth coverage of star schema aggregate tables. Dubbed by Ralph Kimball as the most effective technique for maximizing star schema performance, dimensional aggregates are a powerful and efficient tool that can accelerate data warehouse queries more dramatically than any other technology. After you ensure that a database is properly designed, configured, and tuned, any measures you take to address data warehouse performance should begin with aggregates. Yet, many businesses ignore aggregates, instead turning to specialized, proprietary hardware and software products to solve performance problems. This book fills the knowledge gap that has led businesses on this expensive and risky path.

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Data Warehousing OLAP and Data Mining

S. Nagabhushana "Data Warehousing OLAP and Data Mining"
New Age International | English | | ISBN: N/A; EAN: 9788122417647 | 350 pages | PDF | 1,7 MB

This book is mainly intended for IT students and professionals to learn or implement data warehousing technologies. It experiences the real-time environment and promotes planning, managing, designing, implementing, supporting, maintaining and analyzing data warehouse in organizations and it also provides various mining techniques as well as issues in practical use of Data Mining Tools.

The book is designed for the target audience such as specialists, trainers and IT users. It does not assume any special knowledge as background. Understanding of computer use, databases and statistics will be helpful.
About the Author(s):
Nagabushna Sujihal was born at Bellary in the year 1982 and completed his primary education at National School, Bangalore. He had his Engineering graduation in Electronic and Communication Engineering from Bellary Engineering College, affiliated to Visveswaraih Technological University, Belgaum, Karnataka in the year 2003. Soon after his degree in Engineering, he joined teaching profession at Intell Engineering College, Anantpur, Andhra Pradesh. At present, he is perceiving his post-graduation at Sathayabhama Deemed University, Chennai in Computer Science Engineering.

Contents:
VOLUME I: DATA WAREHOUSING IMPLEMENTATION AND OLAP

PART I: INTRODUCTION The Enterprise IT Architecture
Data Warehouse Concepts
PART II: PEOPLE The Project Sponsor
The CIO
The Project Manager
PART III: PROCESS Warehousing Strategy
Warehouse Management and Support Processes
Data Warehouse Planning
Data Warehouse Implementation
PART IV: TECHNOLOGY Hardware and Operating Systems
Warehousing Software
Warehouse Schema Design
Warehouse Metadata
Warehousing Applications
PART V: MAINTENANCE, EVOLUTION AND TRENDS Warehouse Maintenance and Evolution
Warehousing Trends
PART VI: ON-LINE ANALYTICAL PROCESSING Introduction
OLAP Application
VOLUME II: DATA MINING Introduction
Data Mining with Decision Trees
Data Mining with Association Rules
Automatic Clustering Detection
Data Mining with Neural Network

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A Manager's Guide to Data Warehousing

Laura Reeves, "A Manager's Guide to Data Warehousing"
Wiley | 2009-05-26 | ISBN: 0470176385 | 480 pages | PDF | 2,4 MB

Aimed at helping business and IT managers clearly communicate with each other, this helpful book addresses concerns straight-on and provides practical methods to building a collaborative data warehouse . You’ll get clear explanations of the goals and objectives of each stage of the data warehouse lifecycle while learning the roles that both business managers and technicians play at each stage. Discussions of the most critical decision points for success at each phase of the data warehouse lifecycle help you understand ways in which both business and IT management can make decisions that best meet unified objectives.

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Data Munging with Perl by David Cross

Data Munging with Perl by David Cross
Publisher: Manning Publications | Number Of Pages: 300 | Publication Date: 2001-01-15 | ISBN: 1930110006 | PDF | 1 Mb

Techniques for using Perl to recognize, parse, transform, and filter data.

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Oracle 10g Data Warehousing

Lilian Hobbs, Oracle 10g Data Warehousing
Digital Press | ISBN: 1555583229 | 2004 | PDF | 872 pages | 18.11 MB

Oracle 10g Data Warehousing is a guide to using the Data Warehouse features in the latest version of Oracle Oracle Database 10g. Written by people on the Oracle development team that designed and implemented the code and by people with industry experience implementing warehouses using Oracle technology, this thoroughly updated and extended edition provides an insiders view of how the Oracle Database 10g software is best used for your application.

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Knowledge Discovery and Data Mining: Challenges and Realities

Xingquan Zhu "Knowledge Discovery and Data Mining: Challenges and Realities"
IGI Global | 2007-06-04 | ISBN: 1599042525 | 274 pages | PDF | 3,4 MB

Knowledge discovery and data mining (KDD) is dedicated to exploring meaningful information from a large volume of data. Knowledge Discovery and Data Mining: Challenges and Realities is the most comprehensive reference publication for researchers and real-world data mining practitioners to advance knowledge discovery from low-quality data. This Premier Reference Source presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying, low-quality data. International experts in the field of data mining have contributed all-inclusive chapters focusing on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing.

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Handbook of Research on Text and Web Mining Technologies

Min Song, Yi-Fang Brook Wu, "Handbook of Research on Text and Web Mining Technologies"
Information Science Reference | 2008 | ISBN: 1599049902 | 964 pages | PDF | 11,1 MB

The massive daily overflow of electronic data to information seekers creates the need for better ways to digest and organize this information to make it understandable and useful. Text mining, a variation of data mining, extracts desired information from large, unstructured text collections stored in electronic forms.
The Handbook of Research on Text and Web Mining Technologies is the first comprehensive reference to the state of research in the field of text mining, serving a pivotal role in educating practitioners in the field. This compendium of pioneering studies from leading experts is essential to academic reference collections and introduces researchers and students to cutting-edge techniques for gaining knowledge discovery from unstructured text.

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Data Mining and Knowledge Discovery Handbook

Data Mining and Knowledge Discovery Handbook
Publisher: Springer | ISBN: 0387244352 | edition 2005 | PDF | 1419 pages | 14 mb

This handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified whole. The book first surveys, then provides comprehensive yet concise algorithmic descriptions of classic methods plus recently-developed extensions and novel methods. The volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level computer science and engineering students. It is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.

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Data mining and warehousing

S. Prabhu, N. Venatesan., “Data mining and warehousing”
New Age International | 2007 | ISBN: 8122419720 | 144 pages | PDF | 1 MB

This book is intended as a text in data mining and warehousing for engineering and post-graduate
level students.
We have attempted to cover the major topics in data mining and warehousing in depth.
Advanced material, however, has been put into separate chapters so that courses in a variety of
levels can be taught from this book. A brief synopsis of the chapters and comments on their
appropriateness in a basic course is therefore appropriate.
Chapter 1 introduces the basic concepts to data mining and warehousing and its essentials.
Chapter 2 covers the types of knowledge and learning concepts, which are essential to data
mining techniques.
Chapter 3 covers how to discover the knowledge hidden in the databases using various
techniques.
Chapter 4 deals with various types of data mining techniques and algorithms available.
Chapter 5 covers various applications related to data mining techniques and real time usage of
these techniques in various fields like business, commercial organizations, etc.
Chapter 6 deals with the evaluation of data warehouse and need of the new design process
for databases.
Chapter 7 covers how to design the data warehouse using fact tables, dimension tables and
schemas.
Chapter 8 deals with partitioning strategy in software and hardware.
Chapter 9 covers the data mart and meta data.
Chapter 10 covers backup and recovery process.
Chapter 11 deals with performance, tuning of data warehouse, challenges, benefits and new
architecture of the data warehouse.
This book also contains sufficient material to make up an advanced course on data mining and
warehousing. Each and every chapter has exercise. Model question paper is also given. We conclude
that, this book is the only book, which deals both data mining techniques and data warehousing
concepts suited for the modern time.

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Applied Data Mining: Statistical Methods for Business and Industry

Paolo Giudici, "Applied Data Mining: Statistical Methods for Business and Industry"
Wiley; 1 edition | 2003-10-17 | ISBN: 047084678X | 376 pages | PDF | 2,7 MB

Data mining can be defined as the process of selection, exploration and modelling of large databases, in order to discover models and patterns. The increasing availability of data in the current information society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract such knowledge from data. Applications occur in many different fields, including statistics, computer science, machine learning, economics, marketing and finance.
This book is the first to describe applied data mining methods in a consistent statistical framework, and then show how they can be applied in practice. All the methods described are either computational, or of a statistical modelling nature. Complex probabilistic models and mathematical tools are not used, so the book is accessible to a wide audience of students and industry professionals. The second half of the book consists of nine case studies, taken from the author's own work in industry, that demonstrate how the methods described can be applied to real problems.
Provides a solid introduction to applied data mining methods in a consistent statistical framework
Includes coverage of classical, multivariate and Bayesian statistical methodology
Includes many recent developments such as web mining, sequential Bayesian analysis and memory based reasoning
Each statistical method described is illustrated with real life applications
Features a number of detailed case studies based on applied projects within industry
Incorporates discussion on software used in data mining, with particular emphasis on SAS
Supported by a website featuring data sets, software and additional material
Includes an extensive bibliography and pointers to further reading within the text
Author has many years experience teaching introductory and multivariate statistics and data mining, and working on applied projects within industry
A valuable resource for advanced undergraduate and graduate students of applied statistics, data mining, computer science and economics, as well as for professionals working in industry on projects involving large volumes of data - such as in marketing or financial risk management.

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