Data Mining: High-impact Strategies

Data Mining: High-impact Strategies Review

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Data Mining: High-impact Strategies Feature

Data mining (the analysis step of the Knowledge Discovery in Databases process, or Kdd), a relatively young and interdisciplinary field of computer science, is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management.

With recent technical advances in processing power, storage capacity, and inter-connectivity of computer technology, data mining is seen as an increasingly important tool by modern business to transform unprecedented quantities of digital data into business intelligence giving an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific discovery. The growing consensus that data mining can bring real value has led to an explosion in demand for novel data mining technologies.

This book is your ultimate resource for Data Mining. Here you will find the most up-to-date information, analysis, background and everything you need to know.

In easy to read chapters, with extensive references and links to get you to know all there is to know about Data Mining right away, covering: Data mining, Able Danger, Accuracy paradox, Affinity analysis, Alpha algorithm, Anomaly detection, Apatar, Apriori algorithm, Association rule learning, Automatic distillation of structure, Ball tree, Biclustering, Big data, Biomedical text mining, Business analytics, Canape, Cluster analysis, Clustering high-dimensional data, Co-occurrence networks, Concept drift, Concept mining, Consensus clustering, Correlation clustering, Cross Industry Standard Process for Data Mining, Cyber spying, Data Applied, Data classification (business intelligence), Data dredging, Data fusion, Data mining agent, Data Mining and Knowledge Discovery,

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Data Warehousing: High-impact Strategies

Data Warehousing: High-impact Strategies Review

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Data Warehousing: High-impact Strategies Feature

A data warehouse (Dw) is a database used for reporting. The data is uploaded from the operational systems for reporting. The data may pass through an operational data store for additional operations before it is used in the Dw for reporting.

A data warehouse maintains its functions in three layers: staging, integration, and access. Staging is used to store raw data for use by developers (analysis and support). The integration layer is used to integrate data and to have a level of abstraction from users. The access layer is for getting data out for users.

This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support (Marakas & Obrien 2009). However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform and load data into the repository, and tools to manage and retrieve metadata.

This book is your ultimate resource for Data Warehousing. Here you will find the most up-to-date information, analysis, background and everything you need to know.

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Handbook of Educational Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Handbook of Educational Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) Review

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Handbook of Educational Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) Feature

Handbook of Educational Data Mining (EDM) provides a thorough overview of the current state of knowledge in this area. The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems that EDM has addressed.

Researchers at the Forefront of the Field Discuss Essential Topics and the Latest Advances
With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It brings the educational and data mining communities together, helping education experts understand what types of questions EDM can address and helping data miners understand what types of questions are important to educational design and educational decision making.

Encouraging readers to integrate EDM into their research and practice, this timely handbook offers a broad, accessible treatment of essential EDM techniques and applications. It provides an excellent first step for newcomers to the EDM community and for active researchers to keep abreast of recent developments in the field.

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Visualizing Data: Exploring and Explaining Data with the Processing Environment

Visualizing Data: Exploring and Explaining Data with the Processing Environment Review

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Visualizing Data: Exploring and Explaining Data with the Processing Environment Feature

Enormous quantities of data go unused or underused today, simply because people can't visualize the quantities and relationships in it. Using a downloadable programming environment developed by the author, Visualizing Data demonstrates methods for representing data accurately on the Web and elsewhere, complete with user interaction, animation, and more.

How do the 3.1 billion A, C, G and T letters of the human genome compare to those of a chimp or a mouse? What do the paths that millions of visitors take through a web site look like? With Visualizing Data, you learn how to answer complex questions like these with thoroughly interactive displays. We're not talking about cookie-cutter charts and graphs. This book teaches you how to design entire interfaces around large, complex data sets with the help of a powerful new design and prototyping tool called "Processing".

Used by many researchers and companies to convey specific data in a clear and understandable manner, the Processing beta is available free. With this tool and Visualizing Data as a guide, you'll learn basic visualization principles, how to choose the right kind of display for your purposes, and how to provide interactive features that will bring users to your site over and over. This book teaches you:

• The seven stages of visualizing data -- acquire, parse, filter, mine, represent, refine, and interact
• How all data problems begin with a question and end with a narrative construct that provides a clear answer without extraneous details
• Several example projects with the code to make them work
• Positive and negative points of each representation discussed. The focus is on customization so that each one best suits what you want to convey about your data set

The book does not provide ready-made "visualizations" that can be plugged into any data set. Instead, with chapters divided by types of data rather than types of display, you'll learn how each visualization conveys the unique properties of the data it represents -- why the data was collected, what's interesting about it, and what stories it can tell. Visualizing Data teaches you how to answer questions, not simply display information.

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

Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management Review

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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management Feature

The leading introductory book on data mining, fully updated and revised!

When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition—more than 50% new and revised— is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. 

• Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems
• Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately
• Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more
• Provides best practices for performing data mining using simple tools such as Excel

Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.

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