Showing posts with label Computers. Show all posts
Showing posts with label Computers. Show all posts

πŸ“– Data Mining

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Data Mining
By:Robert Stahlbock,Sven F. Crone,Stefan Lessmann
Published on 2009-11-10 by Springer Science & Business Media


Over the course of the last twenty years, research in data mining has seen a substantial increase in interest, attracting original contributions from various disciplines including computer science, statistics, operations research, and information systems. Data mining supports a wide range of applications, from medical decision making, bioinformatics, web-usage mining, and text and image recognition to prominent business applications in corporate planning, direct marketing, and credit scoring. Research in information systems equally reflects this inter- and multidisciplinary approach, thereby advocating a series of papers at the intersection of data mining and information systems research. This special issue of Annals of Information Systems contains original papers and substantial extensions of selected papers from the 2007 and 2008 International Conference on Data Mining (DMIN’07 and DMIN’08, Las Vegas, NV) that have been rigorously peer-reviewed. The issue brings together topics on both information systems and data mining, and aims to give the reader a current snapshot of the contemporary research and state of the art practice in data mining.

This Book was ranked at 37 by Google Books for keyword mining.

Book ID of Data Mining's Books is 9WHNWT7HoNQC, Book which was written byRobert Stahlbock,Sven F. Crone,Stefan Lessmannhave ETAG "h52D5DvvyRo"

Book which was published by Springer Science & Business Media since 2009-11-10 have ISBNs, ISBN 13 Code is 9781441912800 and ISBN 10 Code is 1441912800

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Modeling and Data Mining in Blogosphere
By:Nitin Agarwal,Huan Liu
Published on 2009 by Morgan & Claypool Publishers


This book offers a comprehensive overview of the various concepts and research issues about blogs or weblogs. It introduces techniques and approaches, tools and applications, and evaluation methodologies with examples and case studies. Blogs allow people to express their thoughts, voice their opinions, and share their experiences and ideas. Blogs also facilitate interactions among individuals creating a network with unique characteristics. Through the interactions individuals experience a sense of community. We elaborate on approaches that extract communities and cluster blogs based on information of the bloggers. Open standards and low barrier to publication in Blogosphere have transformed information consumers to producers, generating an overwhelming amount of ever-increasing knowledge about the members, their environment and symbiosis. We elaborate on approaches that sift through humongous blog data sources to identify influential and trustworthy bloggers leveraging content and network information. Spam blogs or |splogs| are an increasing concern in Blogosphere and are discussed in detail with the approaches leveraging supervised machine learning algorithms and interaction patterns. We elaborate on data collection procedures, provide resources for blog data repositories, mention various visualization and analysis tools in Blogosphere, and explain conventional and novel evaluation methodologies, to help perform research in the Blogosphere. The book is supported by additional material, including lecture slides as well as the complete set of figures used in the book, and the reader is encouraged to visit the book website for the latest information: http: //tinyurl.com/mcp-agarwal Table of Contents: Modeling Blogosphere / Blog Clustering and Community Discovery / Influence and Trust / Spam Filtering in Blogosphere / Data Collection and Evaluation

This Book was ranked at 32 by Google Books for keyword mining.

Book ID of Modeling and Data Mining in Blogosphere's Books is FD3w9fcN-CkC, Book which was written byNitin Agarwal,Huan Liuhave ETAG "A9utMTir0aU"

Book which was published by Morgan & Claypool Publishers since 2009 have ISBNs, ISBN 13 Code is 9781598299083 and ISBN 10 Code is 1598299085

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Mathematics of Big Data
By:Jeremy Kepner,Hayden Jananthan
Published on 2018-07-13 by MIT Press


The first book to present the common mathematical foundations of big data analysis across a range of applications and technologies. Today, the volume, velocity, and variety of data are increasing rapidly across a range of fields, including Internet search, healthcare, finance, social media, wireless devices, and cybersecurity. Indeed, these data are growing at a rate beyond our capacity to analyze them. The tools—including spreadsheets, databases, matrices, and graphs—developed to address this challenge all reflect the need to store and operate on data as whole sets rather than as individual elements. This book presents the common mathematical foundations of these data sets that apply across many applications and technologies. Associative arrays unify and simplify data, allowing readers to look past the differences among the various tools and leverage their mathematical similarities in order to solve the hardest big data challenges. The book first introduces the concept of the associative array in practical terms, presents the associative array manipulation system D4M (Dynamic Distributed Dimensional Data Model), and describes the application of associative arrays to graph analysis and machine learning. It provides a mathematically rigorous definition of associative arrays and describes the properties of associative arrays that arise from this definition. Finally, the book shows how concepts of linearity can be extended to encompass associative arrays. Mathematics of Big Data can be used as a textbook or reference by engineers, scientists, mathematicians, computer scientists, and software engineers who analyze big data.

This Book was ranked at 2 by Google Books for keyword data.

Book ID of Mathematics of Big Data's Books is 4vBoDwAAQBAJ, Book which was written byJeremy Kepner,Hayden Jananthanhave ETAG "2kj770lfl34"

Book which was published by MIT Press since 2018-07-13 have ISBNs, ISBN 13 Code is 9780262347914 and ISBN 10 Code is 0262347911

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πŸ“– Let Them Eat Data

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Let Them Eat Data
By:C. A. Bowers
Published on 2000 by University of Georgia Press


Do computers foster cultural diversity? Ecological sustainability? In our age of high-tech euphoria we seem content to leave tough questions like these to the experts. That dangerous inclination is at the heart of this important examination of the commercial and educational trends that have left us so uncritically optimistic about global computing. Contrary to the attitudes that have been marketed and taught to us, says C. A. Bowers, the fact is that computers operate on a set of Western cultural assumptions and a market economy that drives consumption. Our indoctrination includes the view of global computing innovations as inevitable and on a par with social progress--a perspective dismayingly suggestive of the mindset that engendered the vast cultural and ecological disruptions of the industrial revolution and world colonialism. In Let Them Eat Data Bowers discusses important issues that have fallen into the gap between our perceptions and the realities of global computing, including the misuse of the theory of evolution to justify and legitimate the global spread of computers, and the ecological and cultural implications of unmooring knowledge from its local contexts as it is digitized, commodified, and packaged for global consumption. He also suggests ways that educators can help us think more critically about technology. Let Them Eat Data is essential reading if we are to begin democratizing technological decisions, conserving true cultural diversity and intergenerational forms of knowledge, and living within the limits and possibilities of the earth’s natural systems.

This Book was ranked at 32 by Google Books for keyword data.

Book ID of Let Them Eat Data's Books is kLqzz-NV96cC, Book which was written byC. A. Bowershave ETAG "VXiTnAEjSsU"

Book which was published by University of Georgia Press since 2000 have ISBNs, ISBN 13 Code is 9780820322292 and ISBN 10 Code is 0820322296

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Book which have "216 Pages" is Printed at BOOK under CategoryComputers

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πŸ“– Master Data Management

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Master Data Management
By:David Loshin
Published on 2010-07-28 by Morgan Kaufmann


The key to a successful MDM initiative isn’t technology or methods, it’s people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect. Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you’ll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness. * Presents a comprehensive roadmap that you can adapt to any MDM project. * Emphasizes the critical goal of maintaining and improving data quality. * Provides guidelines for determining which data to “master. * Examines special issues relating to master data metadata. * Considers a range of MDM architectural styles. * Covers the synchronization of master data across the application infrastructure.

This Book was ranked at 34 by Google Books for keyword data.

Book ID of Master Data Management's Books is dJtmVz3nrOUC, Book which was written byDavid Loshinhave ETAG "/gi1CuYXkws"

Book which was published by Morgan Kaufmann since 2010-07-28 have ISBNs, ISBN 13 Code is 9780080921211 and ISBN 10 Code is 0080921213

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Succeeding in Business with Microsoft Excel 2013: A Problem-Solving Approach
By:Debra Gross,Frank Akaiwa,Karleen Nordquist
Published on 2013-08-21 by Cengage Learning


SUCCEEDING IN BUSINESS WITH MICROSOFT OFFICE EXCEL 2013 prepares your students to solve business problems by moving beyond the basic point and click skills to think critically about realistic business situations. When students combine software analysis with their own decision making abilities, they are more likely meet any business challenge with success. The Succeeding in Business Series emphasizes problem-solving, critical thinking, and analysis - challenging students to find efficient and effective solutions. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

This Book was ranked at 16 by Google Books for keyword problem.

Book ID of Succeeding in Business with Microsoft Excel 2013: A Problem-Solving Approach's Books is COriAwAAQBAJ, Book which was written byDebra Gross,Frank Akaiwa,Karleen Nordquisthave ETAG "1qEx0lSbdSE"

Book which was published by Cengage Learning since 2013-08-21 have ISBNs, ISBN 13 Code is 9781285963969 and ISBN 10 Code is 1285963962

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Mastering Text Mining with R
By:Ashish Kumar,Avinash Paul
Published on 2016-12-28 by Packt Publishing Ltd


Master text-taming techniques and build effective text-processing applications with R About This Book Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide Gain in-depth understanding of the text mining process with lucid implementation in the R language Example-rich guide that lets you gain high-quality information from text data Who This Book Is For If you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytics with R, then this book is for you. Exposure to working with statistical methods and language processing would be helpful. What You Will Learn Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process Access and manipulate data from different sources such as JSON and HTTP Process text using regular expressions Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R Discover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA) Build a baseline sentence completing application Perform entity extraction and named entity recognition using R In Detail Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages. Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media. Style and approach This book takes a hands-on, example-driven approach to the text mining process with lucid implementation in R.

This Book was ranked at 12 by Google Books for keyword mining.

Book ID of Mastering Text Mining with R's Books is YNDcDgAAQBAJ, Book which was written byAshish Kumar,Avinash Paulhave ETAG "4PyECq72C4I"

Book which was published by Packt Publishing Ltd since 2016-12-28 have ISBNs, ISBN 13 Code is 9781782174707 and ISBN 10 Code is 1782174702

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πŸ“– The Problem With Software

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The Problem With Software
By:Adam Barr
Published on 2018-10-23 by MIT Press


An industry insider explains why there is so much bad software—and why academia doesn't teach programmers what industry wants them to know. Why is software so prone to bugs? So vulnerable to viruses? Why are software products so often delayed, or even canceled? Is software development really hard, or are software developers just not that good at it? In The Problem with Software, Adam Barr examines the proliferation of bad software, explains what causes it, and offers some suggestions on how to improve the situation. For one thing, Barr points out, academia doesn't teach programmers what they actually need to know to do their jobs: how to work in a team to create code that works reliably and can be maintained by somebody other than the original authors. As the size and complexity of commercial software have grown, the gap between academic computer science and industry has widened. It's an open secret that there is little engineering in software engineering, which continues to rely not on codified scientific knowledge but on intuition and experience. Barr, who worked as a programmer for more than twenty years, describes how the industry has evolved, from the era of mainframes and Fortran to today's embrace of the cloud. He explains bugs and why software has so many of them, and why today's interconnected computers offer fertile ground for viruses and worms. The difference between good and bad software can be a single line of code, and Barr includes code to illustrate the consequences of seemingly inconsequential choices by programmers. Looking to the future, Barr writes that the best prospect for improving software engineering is the move to the cloud. When software is a service and not a product, companies will have more incentive to make it good rather than “good enough to ship.|

This Book was ranked at 38 by Google Books for keyword problem.

Book ID of The Problem With Software's Books is DWA0tQEACAAJ, Book which was written byAdam Barrhave ETAG "66ZAp3TDZK4"

Book which was published by MIT Press since 2018-10-23 have ISBNs, ISBN 13 Code is 9780262038515 and ISBN 10 Code is 026203851X

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Visual Data Mining
By:Simeon Simoff,Michael H. BΓΆhlen,Arturas Mazeika
Published on 2008-07-23 by Springer


Visual Data Mining—Opening the Black Box Knowledge discovery holds the promise of insight into large, otherwise opaque datasets. Thenatureofwhatmakesaruleinterestingtoauserhasbeendiscussed 1 widely but most agree that it is a subjective quality based on the practical u- fulness of the information. Being subjective, the user needs to provide feedback to the system and, as is the case for all systems, the sooner the feedback is given the quicker it can in?uence the behavior of the system. There have been some impressive research activities over the past few years but the question to be asked is why is visual data mining only now being - vestigated commercially? Certainly, there have been arguments for visual data 2 mining for a number of years – Ankerst and others argued in 2002 that current (autonomous and opaque) analysis techniques are ine?cient, as they fail to - rectly embed the user in dataset exploration and that a better solution involves the user and algorithm being more tightly coupled. Grinstein stated that the “current state of the art data mining tools are automated, but the perfect data mining tool is interactive and highly participatory,” while Han has suggested that the “data selection and viewing of mining results should be fully inter- tive, the mining process should be more interactive than the current state of the 2 art and embedded applications should be fairly automated . ” A good survey on 3 techniques until 2003 was published by de Oliveira and Levkowitz .

This Book was ranked at 41 by Google Books for keyword mining.

Book ID of Visual Data Mining's Books is gAtrCQAAQBAJ, Book which was written bySimeon Simoff,Michael H. BΓΆhlen,Arturas Mazeikahave ETAG "7zMtpD0URtQ"

Book which was published by Springer since 2008-07-23 have ISBNs, ISBN 13 Code is 9783540710806 and ISBN 10 Code is 3540710809

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Big Data Now: 2012 Edition
By:O'Reilly Media, Inc.
Published on 2012-10-23 by |O'Reilly Media, Inc.|


The Big Data Now anthology is relevant to anyone who creates, collectsor relies upon data. It's not just a technical book or just a businessguide. Data is ubiquitous and it doesn't pay much attention toborders, so we've calibrated our coverage to follow it wherever itgoes. In the first edition of Big Data Now, the O'Reilly team tracked thebirth and early development of data tools and data science. Now, withthis second edition, we're seeing what happens when big data grows up:how it's being applied, where it's playing a role, and theconsequences -- good and bad alike -- of data's ascendance. We've organized the second edition of Big Data Now into five areas: Getting Up to Speed With Big Data -- Essential information on thestructures and definitions of big data. Big Data Tools, Techniques, and Strategies -- Expert guidance forturning big data theories into big data products. The Application of Big Data -- Examples of big data in action,including a look at the downside of data. What to Watch for in Big Data -- Thoughts on how big data will evolveand the role it will play across industries and domains. Big Data and Health Care -- A special section exploring thepossibilities that arise when data and health care come together.

This Book was ranked at 27 by Google Books for keyword data.

Book ID of Big Data Now: 2012 Edition's Books is DhATUBDFyI0C, Book which was written byO'Reilly Media, Inc.have ETAG "N61h3OfcKi0"

Book which was published by |O'Reilly Media, Inc.| since 2012-10-23 have ISBNs, ISBN 13 Code is 9781449356682 and ISBN 10 Code is 1449356680

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