Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
Автор: Brackett Michael Название: Data Resource Design ISBN: 1935504339 ISBN-13(EAN): 9781935504337 Издательство: Gazelle Book Services Рейтинг: Цена: 10937.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Are you struggling with the formal design of your organisations data resource? Do you find yourself forced into generic data architectures and universal data models? Do you find yourself warping the business to fit a purchased application? Do you find yourself pushed into developing physical databases without formal logical design? Do you find disparate data throughout the organisation? If the answer to any of these questions is Yes, then you need to read Data Resource Design to help guide you through a formal design process that produces a high quality data resource within a single common data architecture. Most public and private sector organisations do not consistently follow a formal data resource design process that begins with the organisations perception of the business world, proceeds through logical data design, through physical data design, and into implementation. Most organisations charge ahead with physical database implementation, physical package implementation, and other brute-force-physical approaches. The result is a data resource that becomes disparate and does not fully support the organisation in its business endeavours. This book describes how to formally design an organisations data resource to meet its current and future business information demand. It builds on "Data Resource Simplexity", which described how to stop the burgeoning data disparity, and on "Data Resource Integration", which described how to understand and resolve an organisations disparate data resource. It describes the concepts, principles, and techniques for building a high quality data resource based on an organisations perception of the business world in which they operate. Like "Data Resource Simplexity" and "Data Resource Integration", Michael Brackett draws on five decades of data management experience building and managing data resources, and resolving disparate data in both public and private sector organisations. He leverages theories, concepts, principles, and techniques from a wide variety of disciplines, such as human dynamics, mathematics, physics, chemistry, philosophy, and biology, and applies them to properly designing data as a critical resource of an organisation. He shows how to understand the business environment where an organisation operates and design a data resource that supports the organisation in that business environment.
Автор: Il Yeol Song; Johann Eder; Tho Manh Nguyen Название: Data Warehousing and Knowledge Discovery ISBN: 3540745521 ISBN-13(EAN): 9783540745525 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the refereed proceedings of the 8th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2007, held in Regensburg, Germany, September 3-7, 2007. This book presents 44 revised full papers that were reviewed and selected from 150 submissions.
Автор: Graham Shawn Et Al Название: Exploring Big Historical Data: The Historian`S Macroscope ISBN: 1783266376 ISBN-13(EAN): 9781783266371 Издательство: World Scientific Publishing Рейтинг: Цена: 5069.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Digital Humanities have arrived at a moment when digital Big Data is becoming more readily available, opening exciting new avenues of inquiry but also new challenges. This pioneering book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines, or designing and using their own tools to process large amounts of information.Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future. It represents the current state-of-the-art thinking in the field and exemplifies the way that digital work can enhance public engagement in the humanities.Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings.The companion website to Exploring Big Historical Data can be found at www.themacroscope.org/. On this site you will find code, a discussion forum, essays, and datafiles that accompany this book.
Автор: Kent William Название: Data & Reality ISBN: 1935504215 ISBN-13(EAN): 9781935504214 Издательство: Gazelle Book Services Рейтинг: Цена: 10723.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Let's step back to the year 1978. Sony introduces hip portable music with the Walkman, Illinois Bell Company releases the first mobile phone, Space Invaders kicks off the video game craze, and William Kent writes Data and Reality. We have made amazing progress in the last four decades in terms of portable music, mobile communication, and entertainment, making devices such as the original Sony Walkman and suitcase-sized mobile phones museum pieces today. Yet remarkably, the book Data and Reality is just as relevant to the field of data management today as it was in 1978.
Data and Reality gracefully weaves the disciplines of psychology and philosophy with data management to create timeless takeaways on how we perceive and manage information. Although databases and related technology have come a long way since 1978, the process of eliciting business requirements and how we think about information remains constant. This book will provide valuable insights whether you are a 1970s data-processing expert or a modern-day business analyst, data modeler, database administrator, or data architect.
This third edition of Data and Reality differs substantially from the first and second editions. Data modeling thought leader Steve Hoberman has updated many of the original examples and references and added his commentary throughout the book, including key points at the end of each chapter.
The important takeaways in this book are rich with insight yet presented in a conversational and easy-to-grasp writing style. Here are just a few of the issues this book tackles:
Has "business intelligence" replaced "artificial intelligence"?
Why is a map's geographic landscape analogous to a data model's information landscape?
Where do forward and reverse engineering fit in our thought process?
Why are we all becoming "data archeologists"?
What causes the communication chasm between the business professional and the information technology professional in most organizations, and how can the logical data model help bridge this chasm?
Why do we invest in hardware and software to solve business problems before determining what the business problems are in the first place?
What is the difference between oneness, sameness, and categories?
Why does context play a role in every design decision?
Why do the more important attributes become entities or relationships?
Why do symbols speak louder than words?
What's the difference between a data modeler, a philosopher, and an artist?
Why is the 1975 dream of mapping all attributes still a dream today?
What influence does language have on our perception of reality?
Can we distinguish between naming and describing?
Автор: Ralph Hughes Название: Agile Data Warehousing Project Management, ISBN: 0123964636 ISBN-13(EAN): 9780123964632 Издательство: Elsevier Science Рейтинг: Цена: 6230.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Offers an introduction to the method as you would practice it in the project room to build a data mart. This title helps to prepare you to join or lead a team in visualizing, building, and validating a single component to an enterprise data warehouse. It includes strategies for getting actionable requirements from a team`s business partner.
Imagine spending a day with top analytical leaders and asking any question you want. In this book, Wayne Eckerson illustrates analytical best practices by weaving his perspective with commentary from seven directors of analytics who unveil their secrets of success. With an innovative flair, Eckerson tackles a complex subject with clarity and insight. Each of the book's 20 chapters is a stand-alone essay on an analytical topic, yet collectively they form a concise methodology about how to implement a successful analytics program.
From the Foreword by Michael Halbherr, Executive Vice President, Nokia
We are living in a time of radical change. From my vantage point as head of Nokia's Location and Commerce business, I see many business and technical trends shaping our future--and all depend on a new commodity: data. In our mapping business, I see the need to evolve from a road-centric tool to something that allows people to truly understand and maneuver the complexities of a modern city. To accomplish this, we need a lot of data and ways to correlate disparate information into what we call "Smart Data." Analytics is core to what we do, and how we deliver value to customers today and in the future.
I recently spoke to the Nokia board about our data, and some members questioned how we could monetize this asset. Since a few members are executives in the oil industry, I told them that data is the "oil of the future", and that you monetize this new resource the same way you monetize oil, by spending time and money refining it. In our case, we are refining data about people, locations, social interactions, traffic, musical preferences, and so on to bring maps to life.
The analytical leaders profiled in this book demonstrate how to refine data for business gain and innovation. They play a pivotal role by bridging the worlds of business and technology. When supported by the business, they've delivered remarkable solutions that have given their organizations a competitive edge.
I highly recommend this book to anyone who wants to monetize the most important resource of our time: data. It's written in language that both a CEO and a CIO can understand, and carries important lessons no matter what side of the business-technology aisle someone sits.
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