Data Mining
Business Intelligence: The IBM Solution: Data Warehousing and OLAP
Data warehousing and data mining allow decision-makers to leverage information and organizational knowledge to gain competitive advantage. These pages take a non-intimidating look at IBM's Business Intelligence Applications, in particular the Visual Warehouse and the OLAP Server.
Data Mining: Technologies, Techniques, Tools, and Trends
Focusing on a data-centric perspective, this book provides a complete overview of data mining: its uses, methods, current technologies, commercial products, and future challenges.
Machine Learning and Data Mining; Methods & Applications
Master the new computational tools to get the most out of your information system. This practical guide, the first to clearly outline the situation for the benefit of engineers and scientists, provides a straightforward introduction to basic machine learning and data mining methods, covering the analysis of numerical, text, and sound data.
Data Mining Solutions: Methods and Tools for Solving Real-World Problems
This valuable overview explores all aspects of commercial data mining tools that database analysts and technically savvy business managers can use to identify sales trends, evaluate and improve performance, monitor financial fraud, forecast investment returns, and much more.
Mining Very Large Databases With Parallel Processing
It is assumed that the reader has a knowledge roughly equivalent to a first degree (B.Sc.) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience of Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and post-graduate students, particularly database researchers interested in advanced, intelligent database applications and artificial intelligence researchers interested in industrial, real-world applications of machine learning.
Introduction to Data Mining and Knowledge Discovery
Excerpted from the Two Crows report Data Mining'98, Introduction to Data Mining and Knowledge Discovery, 2nd edition is a readable 31-page booklet aimed at business users who want a clear, non-technical overview of the techniques and capabilities of data mining. It's a valuable educational tool for prospective users.
Corporate Information Factory, 2nd Edition
The father of the data warehouse incorporates the latest technologies into his blueprint for integrated decision support systems Having invented the corporate information factory (CIF) to help IT and database managers cut through the jungle of information technologies out there, bestselling author Bill Inmon again teams up with experts Claudia Imhoff and Ryan Sousa to show you how to integrate all key components of the modern information system architecture in a way that meets your evolving business needs.
Data Warehousing for Dummies
If you're looking for a slightly irreverent, humorous yet thorough discussion of data warehousing, then check out Data Warehousing for Dummies.
Data Warehousing, Data Mining, and OLAP
"Data Warehousing" is the nuts-and-bolts guide to designing a data management system using data warehousing, data mining, and online analytical processing (OLAP) and how successfully integrating these three technologies can give business a competitive edge.
Data Mining: A Hands-On Approach for Business Professionals
A market-focused, hands-on data mining guide for business professionals, this book/CD package includes detailed case studies in retail, banking, health care and telecommunications. Includes powerful tools on CD-ROM: trial versions of DataMind, Angoss KnowledgeSEEKER and NeuralWare Predict.
Discovering Data Mining from Concept to Implementation
Through extensive case studies and examples, this book provides practical guidance on all aspects of implementing data mining: technical, business, and social. The book also demonstrates IBM's powerful new intelligent Miner tool and shows how it can be applied.
Predictive Data Mining: A Practical Guide
The book begins by exploring the links between "big data"--the data warehouse built up of multiple databases--and traditional statistics. (The authors defend the methods of big data against traditional statistics, which has usually relied on smaller samples. However, they also look at the sources of error in both disciplines.)
Data Mining Techniques: For Marketing, Sales, and Customer Support
This book is a practical guide to mining business data to help marketers and business managers focus their marketing and sales strategies. It explains how each mining technique works and what kinds of business problems each one can solve.
Data Preparation for Data Mining
Data Preparation for Data Mining addresses an issue unfortunately ignored by most authorities on data mining: data preparation.
Data Mining
A comprehensive quide to data mining techniques and practices.
Data Warehousing and Data Mining for Telecommunications
Through dozens of case studies and real-world examples, this clearly written guide shows telecommunications managers how to build more effective data warehouses without wasting time and money on impractical, untenable approaches.
Data Mining Your Website
This book explains how companies of all sizes can use tools and techniques to analyze and benefit from one of their most valuable new sources of marketing information - usage data from a Web site.
Web Warehousing and Knowledge Management
Rob Mattison cuts through the hype that surrounds data warehousing and data analysis; he explores the software tools that provide the ability--IQ and Aperio--and he reviews IBM's various data mining tools. This book--particularly the section on Online Analytical Processing [OLAP] reports--cries out for information on putting Extensible Markup Language (XML) to work in data warehouse applications.
Data Mining: Building Competitive Advantage
Provides the reader with a comprehensive understanding of data mining concepts as well as showing how to use and make the most of the data mining tools that exist on the market today.
Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
Offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations.
Mastering Data Mining: Art and Science of Customer Relationship Management
A case study—based guide to best practices in commercial data mining. Focuses on achieving business results, placing particular emphasis on customer relationship management.
Data Mining and Knowledge Discovery for Process Monitoring and Control (Advances in Industrial Control)
Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state space-based systems for process monitoring, control and diagnosis. The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.
Data Warehousing With Microsoft SQL Server 7 Technical Reference
This guide simplifies this process by showing you how to use the features of SQL Server 7.0 to design, build, and manage a data warehouse.
Essential Oracle8i Data Warehousing
In an effort to help companies manage what Information Week has referred to as the "Web Data Deluge," Oracle has released Oracle8i (the "i" stands for "Internet"), a major upgrade to their flagship database product that provides powerful new data warehousing capabilities. In this updated and expanded edition of their critically acclaimed Oracle8 Data Warehousing, Gary Dodge and Tim Gorman provide database developers and administrators with complete, detailed coverage of all they need to know to build and manage a fast, high-performance data warehouse using the Oracle8i technology. Step-by-step, they cover all the bases, including designing a data warehouse for optimum performance, building a data warehouse, loading data into the data warehouse, improving warehouse performance with aggregate data, and administering data warehouse performance.
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
This first systematic and self-contained monograph on "Symbolic Data Analysis" presents the most recent methods for analyzing and visualizing symbolic data. It generalizes classical methods of exploratory, statistical and graphical data analysis to the case of complex data where the entries of a data table are, e. g. , sets of categories or of numbers, intervals or probability distributions. Typical methods include: graphical displays using Zoom Stars, visualization and future extraction by symbolic factor analysis, decision trees, discrimination, classification and clustering methods. Several benchmark examples from National Statistical Offices illustrate the usefulness of the methods. The book contains an extensive bibliography and a subject index.
Information Visualization in Data Mining and Knowledge Discovery
Information Visualization in Data Mining and Knowledge Discovery is the first book to explore the fertile ground of uniting data mining and data visualization principles in a new set of knowledge discovery techniques. Leading researchers from the fields of data mining, data visualization, and statistics present findings organized around topics introduced in two recent international knowledge discovery and data mining workshops. Collected and edited by three of the area's most influential figures, these chapters introduce the concepts and components of visualization, detail current efforts to include visualization and user interaction in data mining, and explore the potential for further synthesis of data mining algorithms and data visualization techniques.
Principles of Data Mining
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.
Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms
Recent advances in the data collection and storage technologies have made it possible for companies (e.g. bar-code technology), administrative agencies (e.g. census data) and scientific laboratories (e.g. molecule databases in chemistry or biology) to keep vast amounts of data relating to their activities. At the same time, the availability of cheap computing power has made automatic extraction of structured knowledge from these data feasible. Data mining includes such activities as classification, clustering, similarity analysis, summarization, association rule and sequential pattern discovery, and so forth.
Data Warehouses: More Than Just Mining
Data mining gets plenty of press these days, but before the data can be mined, it must be warehoused assembled, cleaned, organized, and stored. And now that vendors are introducing data warehouses on a smaller scale, even companies with limited resources can use this hot groundbreaking new study which profiles four small to medium-sized companies with data warehouses and reveals how they use this tool to get big paybacks in financial reporting and product quality information.
Data Mining: Concepts and Techniques
Written expressly for database practitioners and professionals, this book begins with a conceptual introduction designed to get you up to speed. This is followed by a comprehensive and state- of-the-art coverage of data mining concepts and techniques. Each chapter functions as a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability, keeping your eye on the issues that will affect your project's results and your overall success.
Advances in Distributed and Parallel Knowledge Discovery
Knowledge discovery and data mining (KDD) deals with the problem of extracting interesting associations, classifiers, clusters, and other patterns from data. The emergence of network-based distributed computing environments has introduced an important new dimension to this problem--distributed sources of data. Traditional centralized KDD typically requires central aggregation of distributed data, which may not always be feasible because of limited network bandwidth, security concerns, scalability problems, and other practical issues. Distributed knowledge discovery (DKD) works with the merger of communication and computation by analyzing data in a distributed fashion. This technology is particularly useful for large heterogeneous distributed environments such as the Internet, intranets, mobile computing environments, and sensor-networks.
Advances in Knowledge Discovery and Data Mining
Advances in Knowledge Discovery and Data Mining brings together the latest research -- in statistics, databases, machine learning, and artificial intelligence -- that are part of the exciting and rapidly growing field of Knowledge Discovery and Data Mining. Topics covered include fundamental issues, classification and clustering, trend and deviation analysis, dependency modeling, integrated discovery systems, next generation database systems, and application case studies.
Data Mining Explained: A Manager's Guide to Customer-Centric Business Intelligence
Data Mining Explained helps technically-proficient managers and IT professionals use powerful data mining technologies to solve important business challenges, most importantly to identify and better serve customer needs. Written by data mining experts, Data Mining Explained describes how companies in general and those in key vertical markets can design and build effective technical marketing and sales strategies and operations using data mining.
Building Data Mining Applications for CRM
Implement data mining for a powerful competitive advantage. Are you fully harnessing the power of information to support management and marketing decisions? You will, with the help of Building Data Mining Applications for CRM, a one-stop guide to choosing the right tools and technologies for a state-of-the-art data management strategy built on a Customer Relationship Management (CRM) framework. Authors Alex Berson, Stephen Smith, and Kurt Thearling help you understand the principles of data warehousing and data mining systems, and carefully spell out techniques for applying them to your firm's greatest benefit. Find out about Online Analytical Processing (OLAP) tools that quickly navigate within your collected data. Explore privacy and legal issues...evaluate current data mining application packages...and let real-world examples show you how data mining can impact, and improve - all of your key business processes.
Microsoft Analysis Solutions: OLAP and Data Mining with Microsoft Analysis Services
A much-needed user manual for Microsoft's groundbreaking OLAP and data mining tool. This up-to-date user guide covers the major new release of Microsoft OLAP Services, now called Microsoft Analysis Services-- the technology that made online data analysis widely available for application developers. A sequel to our successful Microsoft OLAP Solutions, this book provides detailed instructions for installing, using, and managing Microsoft Analysis Services as well as integrating it with other Microsoft data warehouse tools. Coverage of new features includes data mining in analysis services, working with the analysis manager interface, scalability features in analysis servers, and allowing clients to analyze information while disconnected from the server.
Data Mining and Business Intelligence: A Guide to Productivity
Provides an overview of data mining technology and how it is applied in a business environment. This book describes the corresponding data mining methodologies that are used to solve a variety of business problems that enhance firm-level efficiency in a less technical, more managerial style.
Intelligent Data Engineering and Automated Learning - Ideal 2000
Proceedings of the 2nd International Conference on Intelligent Data Engineering and Automated Learning: IDEAL 2000, Data Mining, Financial Engineering, and Intelligent Agents, held in Hong Kong, China, December 13-15, 2000. Softcover.
Microsoft Data Mining
Microsoft Data Mining approaches data mining from particular the perspective of IT professionals using Microsoft data management technologies. The author explains the new data mining capabilities in Microsoft's SQL Server 2000 database, Commerce Server, and other products, details the Microsoft OLE DB for Data Mining standard, and gives readers best practices for using all of them. The book bridges the previously specialized field of data mining with the new technologies and methods that are quickly making it an important mainstream tool for companies of all sizes.
Data Mining with Microsoft SQL Server 2000 Technical Reference
With its state-of-the-art capabilities for rapidly processing and retrieving huge quantities of data, Microsoft(r) SQL Server 2000 is quickly growing in popularity among large corporations. But learning how to take advantage of the powerful, built-in data-mining services in SQL Server to turn all that data into meaningful information takes time and effort. Data Mining with SQL Server 2000 Technical Reference is the ideal, in-depth reference guide for any database developer, administrator, or IT professional who needs comprehensive information about these powerful new data-mining services. In particular, it fully examines the data-warehousing architecture in SQL Server 2000 to show how to take full advantage of the data-mining services in this RDBMS. This is the only Microsoft-approved technical guide to the data mining services in SQL Server 2000.
Financial Business Intelligence: Trends, Technology, Software Selection and Implementation
Business intelligence has recently become a word used by almost every CFO, controller and analyst. After having spent the last decade implementing Enterprise Resource Planning software and other mission-critical solutions, companies now have large databases with transactional data sitting in their computer rooms. Now, finally, the technology has reached a point where it is possible - in almost real time - to quickly and easily analyze the financial data in the corporate databases to be able to make more intelligent business decisions. This book will help financial managers understand the trends, technology, software selection and implementation of financial business intelligence (financial BI) software.
Visual Data Mining: Techniques and Tools for Data Visualization and Mining
Marketing analysts use data mining techniques to gain a reliable understanding of customer buying habits and then use that information to develop new marketing campaigns and products. Visual mining tools introduce a world of possibilities to a much broader and non-technical audience to help them solve common business problems.
Data Mining Using SAS Applications
Most books on data mining focus on principles and furnish few instructions on how to carry out a data mining project. Data Mining Using SAS Applications not only introduces the key concepts but also enables readers to understand and successfully apply data mining methods using powerful yet user-friendly SAS macro-call files. These methods stress the use of visualization to thoroughly study the structure of data and check the validity of statistical models fitted to data. The text furnishes 13 easy- to-use SAS data mining macros designed to work with the standard SAS modules. No additional modules or previous experience in SAS programming is required. The author shows how to perform complete predictive modeling, including data exploration, model fitting, assumption checks, validation, and scoring new data, on SAS datasets in less than ten minutes!
Data Mining: Concepts, Models, Methods and Algorithms
Describes representative state of the art methods and algorithms originating from different disciplines. Offers guidance on how and when to use a particular software tool from among the hundreds offered when faced with a data set to mining.
Business Modeling and Data Mining
This new book provides practical guidance on how to identify and structure real-world business questions in terms that can be answered by quantitative models and data mining. It also addresses what problems data mining can usefully address and how.
Optimal Database Marketing: Strategy, Development, and Data Mining
Contemporary direct marketing and e-commerce companies cannot exist in today’s competitive environment without the use of marketing databases. Databases allow marketers to reach customers and cultivate relationships more effectively and efficiently. Although databases provide a means to establish and enhance relationships, they can also be used incorrectly, inefficiently, and even unethically.
Integrating Results through Meta-Analytic Review Using SAS Software
Finally ... a book addressing the various needs,concepts, and approaches for SAS users who work with meta-analytic procedures! Wang and Bushman introduce the reader to the important concepts in meta-analysis and how to use SAS software for this specific type of analysis. The authors thoroughly describe how meta- analysis can be used in "data-mining" projects to discover meaningful relations among variables in a collection of studies. In addition, the following concepts are covered in detail: how to present your results in graphical format, how to combine effect-size estimates based on categorical and continuous data, vote-counting procedures to show the statistical significance of results, how to combine effect-size estimates and vote-counts, how to deal with fixed- and random-effects models, how to combine dependent or correlated effect-size estimates using multivariate procedures, and how to report the results and conduct the data analysis portion of a meta-analysis.
Data Mining and Knowledge Discovery With Evolutionary Algorithms
This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. In particular, in this book we emphasize the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making.In a nutshell, the motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search performed by most rule induction methods.
Data Mining: A Tutorial Based Primer
Primer on data mining provides an introduction to the principles and techniques for extracting information, from a business- minded executive. Data sets from the CD-ROM are used in examples and exercises.
Modern Data Warehousing, Mining, and Visualization: Core Concepts
Designed for undergraduate/graduate-level courses in Information Systems or Operations and Decision Technologies electives and taking a multidisciplinary user/manager approach, this text looks at data warehousing technologies necessary to support the business processes of the 21st century.
Data Mining for Design and Manufacturing: Methods and Applications
This is the first book that brings together research and applications for data mining within design and manufacturing. The aim of the book is 1) to clarify the integration of data mining in engineering design and manufacturing, 2) to present a wide range of domains to which data mining can be applied, 3) to demonstrate the essential need for symbiotic collaboration of expertise in design and manufacturing, data mining, and information technology, and 4) to illustrate how to overcome central problems in design and manufacturing environments.
Investigative Data Mining for Security and Criminal Detection, First Edition
Introduces security professionals, intelligence and law enforcement analysts, and criminal investigators to the use of data mining as a new kind of investigative tool. The first book to outline how data mining technologies can be used to combat crime in the 21st century.
Bioinformatics for Dummies
- A practical introduction to bioinformatics-computer technologies that biochemical and pharmaceutical researchers use to analyze genetic and biological data
- Shows how to do sophisticated bioinformatic analysis without learning UNIX first
- Helps researchers choose the right bioinformatics tool, use it effectively, and interpret the results
- Guides readers to the most helpful Web resources and freely available tools
- Addresses common biological questions, problems, and projects, while also providing a UNIX/Linux overview and tips on using Perl
- Companion Web site contains the data sets used in the book and useful related links
Implementing Data Mining Solution for an Automobile Insurance Company: Reconciling Theoretical Benefits with Practical Considerations (PDF download)
This case describes an insurance company investigation into the benefits of data mining for an anonymous Australian automobile insurance company. Although the investigation was able to demonstrate quantitative benefits of adopting a data mining approach, there are many practical issues that need to be resolved before the data mining approach can be implemented. This case provides insights into the decision-making process that is involved in exploring emerging technologies.
Data Mining and Computational Intelligence
Presents a comprehensive look at recent advances in the application of soft computing and fuzzy logic theory to data mining and knowledge discovery databases, focusing on some of the more difficult and as yet unsolvable issues of data mining. These include understandability of patterns and change detection in time series.
Medical Data Mining and Knowledge Discovery
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Describes important statistical ideas in machine learning, data mining, and bioinformatics. Covers a broad range, from supervised learning (prediction), to unsupervised learning, including classification trees, neural networks, and support vector machines. DLC: Supervised learning (Machine learning).
Managing Data Mining Technologies in Organizations: Techniques and Applications
Managing Data Mining Technologies in Organizations: Techniques and Applications details the state-of-the-art data mining research, which reflects in a potpourri of chapters that demonstrate diverse use of techniques and their applications for data mining. The chapters illustrate applications of data mining and visualization for credit screening, forecasting, medical diagnosis, genetic algorithms, Bayesian networks, neural networks, and many others. The chapters discuss research results and applications of data mining to deliver rich decision-making information.
Exploratory Data Analysis in Empirical Research
Facing rapidly growing challenges in empirical research, this volume presents a selection of new methods and approaches in the field of Exploratory Data Analysis. The interested reader will find numerous ideas and examples for cross disciplinary applications of classification and data analysis methods in fields such as data and web mining, medicine and biological sciences as well as marketing, finance and management sciences.
Data Mining III
Data Mining brings together techniques from machine learning, pattern recognition, statistics, databases, linguistics and visualization in order to extract information from large databases. Originally principally concerned with behavioural applications, such as the understanding of customer behaviour, its scope has now been widened with the introduction of Text Mining techniques. Areas now encompassed by Data Mining include military, market, and competitive intelligence applications, taxonomies and internet search techniques, and knowledge management applications.
Advances in Data Mining: Applications in E-Commerce, Medicine, and Knowledge Management
Presents six thoroughly reviewed and revised full papers of describing selected prospects on data mining.
Applications of Data Mining to Electronic Commerce
Applications of Data Mining to Electronic Commerce brings together in one place important contributions and up-to-date research results in this fast moving area.
Mining Your Own Business in Retail Using DB2 Intelligent Miner for Data
Mining the Web: Analysis of Hypertext and Semi Structured Data
Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for extracting and producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issues- including Web crawling and indexing-Chakrabarti examines machine learning techniques as they relate specifically to the challenges of Web mining and provides applications of machine learning to systematically acquire, store, and analyze data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress toward a Web that is more aware of content semantics. This thorough and forward-looking book gives the theoretical and practical foundations you need to build innovative applications for mining the Web.
Business Intelligence: The Savvy Manager's Guide
Business Intelligence describes the basic architectural components of a business intelligence environment, ranging from traditional topics such as business process modeling, data modeling, and more modern topics such as business rule systems, data profiling, information compliance and data quality, data warehousing, and data mining. This book progresses through a logical sequence, starting with data model infrastructure, then data preparation, followed by data analysis, integration, knowledge discovery, and finally the actual use of discovered knowledge. The book contains a quick reference guide for business intelligence terminology. Business Intelligence is part of Morgan Kaufmann's Savvy Manager's Guide series.
Web Data Mining and Applications in Business Intelligence and Counter-Terrorism
The explosion of Web-based data has created a demand among executives and technologists for methods to identify, gather, analyze, and utilize data that may be of value to corporations and organizations. The emergence of data mining, and the larger field of Web mining, has businesses lost within a confusing maze of mechanisms and strategies for obtaining and managing crucial intelligence.Web Data Mining and Applications in Business Intelligence and Counter-Terrorism responds by presenting a clear and comprehensive overview of Web mining, with emphasis on CRM and, for the first time, security and counter-terrorism applications. The tools and methods of Web mining are revealed in an easy-to-understand style, emphasizing the importance of practical, hands-on experience in the creation of successful e- business solutions.
Intelligent Data Warehousing: From Data Preparation to Data Mining
This book presents state-of-the-art data warehousing research and practice from an integrated business and computer science perspective - the first monograph to do so - and broadens the scope of data mining by discussing it in terms of data warehousing. The material, rooted in database management systems and artificial intelligence, brings the intelligent techniques associated with AI to the entire process of data warehousing, from preparing data and building data warehousing to analyzing data.
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.
Guide to Enterprise Data Mining
Uncertainty Handling and Quality Assesment in Data Mining
Text provides an introduction to the application of these concepts in Knowledge Discovery and Data Mining. Reviews the state-of-the-art in uncertainty handling and discusses a framework for unveiling and handling uncertainty. For IT professionals involved with data mining and knowledge discovery.
Exploratory Data Mining and Data Cleaning
- Written for practitioners of data mining, data cleaning and database management.
- Presents a technical treatment of data quality including process, metrics, tools and algorithms.
- Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge.
- Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches.
- Uses case studies to illustrate applications in real life scenarios.
- Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques.
Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data
Compilation of essays offering succinct, specific methods for solving the most commonly experienced problems in database marketing.
Mining the Web: Transforming Customer Data
Web sites gather a lot of detailed information about customers. Unfortunately, most companies lack the means to use that information to improve their marketing and customer support functions. Considered by most experts to be the new frontier in the database and data warehousing fields, Web mining solves that problem. Coauthored by two bestselling data mining authors, Mining the Web explains, for corporate decision makers, IT managers, and database marketers, how data mining principles and techniques can be applied to various types of Web sites. More importantly, they describe techniques for using the resulting goldmine of business data to develop more effective advertising campaigns and better customer service.
Data Mining, Practical Machine Learning Tools and Techniques
Like the popular first edition, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining-including both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource. Complementing the authors' instruction, including a fully-revised Chapter 8 and 30 new technique sections, is a fully functional platform-independent Java software system for machine learning, available for downl
Text Mining Application Programming
This book is useful in teaching developers to build text mining applications to manage vast amounts of text and turn it into useful data.
Data Preparation for Data Mining Using SAS
Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little how to information? And are you, like most analysts, preparing the data in SAS?
This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection.
Data Mining and Predictive Analysis
It is now possible to predict the future when it comes to crime. In Data Mining and Predictive Analysis, Dr. Colleen McCue describes not only the possibilities for data mining to assist law enforcement professionals, but also provides real-world examples showing how data mining has identified crime trends, anticipated community hot-spots, and refined resource deployment decisions. In this book Dr. McCue describes her use of "off the shelf" software to graphically depict crime trends and to predict where future crimes are likely to occur. Armed with this data, law enforcement executives can develop "risk-based deployment strategies," that allow them to make informed and cost-efficient staffing decisions based on the likelihood of specific criminal activity.
Making Sense of Data
The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data.
Mining Graph Data
This text takes a focused and comprehensive look at an area of data mining that is quickly rising to the forefront of the field: mining data that is represented as a graph. Each chapter is written by a leading researcher in the field; collectively, the chapters represent the latest findings and applications in both theory and practice, including solutions to many of the algorithmic challenges that arise in mining graph data. Following the authors' step-by-step guidance, even readers with minimal background in analyzing graph data will be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets.
Data Mining for Business Intelligence
Learn how to develop models for classification, prediction, and customer segmentation. This book provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration and affinity analysis; features a business decision-making context for these key methods; and illustrates the application and interpretation of these methods using real business cases and data.
Physical Database Design
Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more.
The rapidly increasing volume of information contained in relational databases places a strain on databases, performance, and maintainability, and DBAs are under greater pressure than ever to optimize database structure for system performance and administration.
Physical Database Design discusses the concept of how physical structures of databases affect performance and includes specific examples, guidelines, and best and worst practices for a variety of DBMSs and configurations. Something as simple as improving the table index design has a profound impact on performance. Every form of relational database, such as Online Transaction Processing (OLTP), Enterprise Resource Management (ERP), Data Mining (DM), or Management Resource Planning (MRP), can be improved using these methods.
Introduction to Data Mining Using SAS Enterprise Miner
If you have an abundance of data, but no idea what to do with it, this book was written for you! Packed with examples from an array of industries, this introductory text provides you with excellent starting points and practical guidelines to begin data mining today. The author encourages you to think of data mining as a process of exploration rather than as a collection of tools to investigate data. In that way, you choose the methods that will extract the most information from your data, and, while there are no right answers to investigating data sets, there are many questions that can be asked to produce meaningful results. Each answer then creates a path that helps you drill down to explore the data fully. It is up to you to determine what is of interest and what is important to analyze.
Data Mining VIII: Data, Text and Web Mining and their Business Applications
Bringing together papers presented at the Eighth International Conference on Data, Text and Web Mining and their Business Applications, this book addresses the new developments in the important field of information engineering. The book, edited by A. Zanasi, (TEMIS Text Mining Solutions, Italy, Italy), C. A. Brebbia (Wessex Institute of Technology, Southampton, UK) and N. F. F. Ebecken (COPPE/Federal University of Rio de Janeiro, Brazil) features contributions on categorization methods; data preparation; enterprise information systems; mining environmental and geospatial data; text mining; applications in business, industry and customer relationship management; and national security.
Full contents details on the book can be found at www.witpressusa.com.
Mining the Talk
Leverage Unstructured Data to Become More Competitive, Responsive, and Innovative
In Mining the Talk, two leading-edge IBM researchers introduce a revolutionary new approach to unlocking the business value hidden in virtually any form of unstructured data-from word processing documents to websites, emails to instant messages.
The authors review the business drivers that have made unstructured data so important-and explain why conventional methods for working with it are inadequate. Then, writing for business professionals-not just data mining specialists-they walk step-by-step through exploring your unstructured data, understanding it, and analyzing it effectively.
