big data mining and analytics

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Big Data Mining And Analytics


Big Data Mining And Analytics


File Size : 43,8 Mb
Total Download : 324

Author : Stephan Kudyba
language : en
Publisher: CRC Press
Release Date : 2014-03-12



Download Big Data Mining And Analytics written by Stephan Kudyba and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-03-12 with Computers categories.


There is an ongoing data explosion transpiring that will make previous creations, collections, and storage of data look trivial. Big Data, Mining, and Analytics: Components of Strategic Decision Making ties together big data, data mining, and analytics to explain how readers can leverage them to extract valuable insights from their data. Facilitating a clear understanding of big data, it supplies authoritative insights from expert contributors into leveraging data resources, including big data, to improve decision making. Illustrating basic approaches of business intelligence to the more complex methods of data and text mining, the book guides readers through the process of extracting valuable knowledge from the varieties of data currently being generated in the brick and mortar and internet environments. It considers the broad spectrum of analytics approaches for decision making, including dashboards, OLAP cubes, data mining, and text mining. Includes a foreword by Thomas H. Davenport, Distinguished Professor, Babson College; Fellow, MIT Center for Digital Business; and Co-Founder, International Institute for Analytics Introduces text mining and the transforming of unstructured data into useful information Examines real time wireless medical data acquisition for today’s healthcare and data mining challenges Presents the contributions of big data experts from academia and industry, including SAS Highlights the most exciting emerging technologies for big data—Hadoop is just the beginning Filled with examples that illustrate the value of analytics throughout, the book outlines a conceptual framework for data modeling that can help you immediately improve your own analytics and decision-making processes. It also provides in-depth coverage of analyzing unstructured data with text mining methods to supply you with the well-rounded understanding required to leverage your information assets into improved strategic decision making.

Predictive Analytics Data Mining And Big Data


Predictive Analytics Data Mining And Big Data


File Size : 43,8 Mb
Total Download : 907

Author : S. Finlay
language : en
Publisher: Springer
Release Date : 2014-07-01



Download Predictive Analytics Data Mining And Big Data written by S. Finlay and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-01 with Business & Economics categories.


This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.

High Performance Data Mining And Big Data Analytics


High Performance Data Mining And Big Data Analytics


File Size : 48,6 Mb
Total Download : 753

Author : Khosrow Hassibi, Ph.d.
language : en
Publisher:
Release Date : 2014-10-07



Download High Performance Data Mining And Big Data Analytics written by Khosrow Hassibi, Ph.d. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-07 with Technology & Engineering categories.


The use of machine learning and data mining to create value from corporate or public data is nothing new. It is not the first time that these technologies are in the spotlight. Many remember the late '80s and the early '90s when machine learning techniques—in particular neural networks—had become very popular. Data mining was at a rise. There were talks everywhere about advanced analysis of data for decision making. Even the popular android character in “Star Trek: The Next Generation” had been named appropriately as “Data.” Data mining science has been the cornerstone of many data products and applications for more than two decades, e.g., in finance and retail. Credit scores have been in use for decades to assess credit worthiness of people when applying for credit or loan. Sophisticated real-time fraud scores based on individual's transaction spending patterns have been used since early '90s to protect credit card holders from a variety of fraud schemes. However, the popularity of web products from the likes of Google, Linked-in, Amazon, and Facebook has helped analytics become a household name. While a decade ago, the masses did not know how their detailed data were being used by corporations for decision making, today they are fully aware of that fact. Many people, especially the millennial generation, voluntarily provide detailed information about themselves. Today people know that any mouse click they generate, any comment they write, any transaction they perform, and any location they go to, may be captured and analyzed for some business purpose. Every new technology comes with lots of hype and many new buzzwords. Often, fact and fiction get mixed-up making it impossible for outsiders to assess the technology's true relevance. I wrote this book to provide an objective view of analytics trends today. I have written it in complete independence, and solely as a personal passion. As a result, the views expressed in this book are those of the author and do not necessarily represent the views of, and should not be attributed to, any vendor or employer.Due to the exponential growth of data, today there is an ever increasing need to process and analyze big data. High-performance computing architectures have been devised to address the need for handling big data, not only from a transaction processing standpoint but also from a tactical and strategic analytics viewpoint. The success of big data analytics in large web companies has created a rush toward understanding the impact of new big data technologies in classic analytics environments that already employ a multitude of legacy analytics technologies. There is a wide variety of readings about big data, high-performance computing for analytics, massively parallel processing (MPP) databases, Hadoop and its ecosystem, algorithms for big data, in-memory databases, implementation of machine learning algorithms for big data platforms, and big data analytics. However, none of these readings provides an overview of these topics in a single document. The objective of this book is to provide a historical and comprehensive view of the recent trend toward high-performance computing technologies, especially as it relates to big data analytics and high-performance data mining. The book also emphasizes the impact of big data on requiring a rethinking of every aspect of the analytics life cycle, from data management, to data mining and analysis, to deployment.As a result of interactions with different stakeholders in classic organizations, I realized there was a need for a more holistic view of big data analytics' impact across classic organizations, and also the impact of high-performance computing techniques on legacy data mining. Whether you are an executive, manager, data scientist, analyst, sales or IT staff, the holistic and broad overview provided in the book will help in grasping the important topics in big data analytics and its potential impact in your organizations.

Big Data Data Mining And Machine Learning


Big Data Data Mining And Machine Learning


File Size : 55,7 Mb
Total Download : 965

Author : Jared Dean
language : en
Publisher: John Wiley & Sons
Release Date : 2014-05-07



Download Big Data Data Mining And Machine Learning written by Jared Dean and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-07 with Computers categories.


With big data analytics comes big insights intoprofitability Big data is big business. But having the data and thecomputational power to process it isn't nearly enough to producemeaningful results. Big Data, Data Mining, and Machine Learning:Value Creation for Business Leaders and Practitioners is acomplete resource for technology and marketing executives lookingto cut through the hype and produce real results that hit thebottom line. Providing an engaging, thorough overview of thecurrent state of big data analytics and the growing trend towardhigh performance computing architectures, the book is adetail-driven look into how big data analytics can be leveraged tofoster positive change and drive efficiency. With continued exponential growth in data and ever morecompetitive markets, businesses must adapt quickly to gain everycompetitive advantage available. Big data analytics can serve asthe linchpin for initiatives that drive business, but only if theunderlying technology and analysis is fully understood andappreciated by engaged stakeholders. This book provides a view intothe topic that executives, managers, and practitioners require, andincludes: A complete overview of big data and its notablecharacteristics Details on high performance computing architectures foranalytics, massively parallel processing (MPP), and in-memorydatabases Comprehensive coverage of data mining, text analytics, andmachine learning algorithms A discussion of explanatory and predictive modeling, and howthey can be applied to decision-making processes Big Data, Data Mining, and Machine Learning providestechnology and marketing executives with the complete resource thathas been notably absent from the veritable libraries of publishedbooks on the topic. Take control of your organization's big dataanalytics to produce real results with a resource that iscomprehensive in scope and light on hyperbole.

Big Data Analytics


Big Data Analytics


File Size : 44,8 Mb
Total Download : 894

Author : Parag Kulkarni
language : en
Publisher: PHI Learning Pvt. Ltd.
Release Date : 2016-07-07



Download Big Data Analytics written by Parag Kulkarni and has been published by PHI Learning Pvt. Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-07 with Language Arts & Disciplines categories.


The book is an unstructured data mining quest, which takes the reader through different features of unstructured data mining while unfolding the practical facets of Big Data. It emphasizes more on machine learning and mining methods required for processing and decision-making. The text begins with the introduction to the subject and explores the concept of data mining methods and models along with the applications. It then goes into detail on other aspects of Big Data analytics, such as clustering, incremental learning, multi-label association and knowledge representation. The readers are also made familiar with business analytics to create value. The book finally ends with a discussion on the areas where research can be explored.

Data Analytics


Data Analytics


File Size : 44,9 Mb
Total Download : 647

Author : Herbert Jones
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-02-04



Download Data Analytics written by Herbert Jones and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-04 with categories.


Say goodbye to scratching your head in confusion This data analytics book could be the answer you're looking for... This book has lots of valuable eye-opening information about data analytics, which will help you understand the concept of data mining, data collection, big data analytics for business and business intelligence concepts. With this book, not only will you understand all the internal nitty-gritties about data analytics, you will also understand why data analytics is changing the business arena. You'll realize that the high-performance analytics will enable you to do stuff that you never thought about before probably because the volumes of data were just too big (among other reasons) and so much more. We'll begin by first examining what data analytics really means and what it entails. Do not fret when you meet challenging terms as you read on, as this book includes detailed explanations of words you may not understand. Here are just some of the topics that are discussed within this book: Overview Of Data Analytics: What Is Data Analytics (And Big Data Analytics)? Data Analytics And Business Intelligence Data Analysis And Data Analytics Data Mining Data Collection Types Of Data Analytics The Process: The Lifecycle Of Big Data Analytics Behavioral Analytics: Using Big Data Analytics To Find Hidden Customer Behavior Patterns Further Pattern Discovery In Advanced Analytics: Machine Learning And Much, Much More Get the book now and learn more about data analytics!

Genomic Signal Processing


Genomic Signal Processing


File Size : 48,6 Mb
Total Download : 770

Author : Ilya Shmulevich
language : en
Publisher: Princeton University Press
Release Date : 2014-09-08



Download Genomic Signal Processing written by Ilya Shmulevich and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-08 with Science categories.


Genomic signal processing (GSP) can be defined as the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Situated at the crossroads of engineering, biology, mathematics, statistics, and computer science, GSP requires the development of both nonlinear dynamical models that adequately represent genomic regulation, and diagnostic and therapeutic tools based on these models. This book facilitates these developments by providing rigorous mathematical definitions and propositions for the main elements of GSP and by paying attention to the validity of models relative to the data. Ilya Shmulevich and Edward Dougherty cover real-world situations and explain their mathematical modeling in relation to systems biology and systems medicine. Genomic Signal Processing makes a major contribution to computational biology, systems biology, and translational genomics by providing a self-contained explanation of the fundamental mathematical issues facing researchers in four areas: classification, clustering, network modeling, and network intervention.