Download or read book Data Mining Models Second Edition written by David L. Olson and published by Business Expert Press. This book was released on 2018-04-25 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to describe the benefits of data mining in business, the process and typical business applications, the workings of basic data mining models, and demonstrate each with widely available free software. The book focuses on demonstrating common business data mining applications. It provides exposure to the data mining process, to include problem identification, data management, and available modeling tools. The book takes the approach of demonstrating typical business data sets with open source software. KNIME is a very easy-to-use tool, and is used as the primary means of demonstration. R is much more powerful and is a commercially viable data mining tool. We also demonstrate WEKA, which is a highly useful academic software, although it is difficult to manipulate test sets and new cases, making it problematic for commercial use.

Download or read book Surgical Pathology of the Head and Neck Second Edition written by Leon Barnes and published by CRC Press. This book was released on 2000-11-29 with total page 852 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated, reorganized, and revised throughout, this highly lauded three-volume reference provides an interdisciplinary approach to the diagnosis, treatment, and management of head and neck diseases, including the incidence, etiology, clinical presentation, pathology, differential diagnosis, and prognosis for each disorder-promoting clear communication between pathologists and surgeons. Written by more than 30 internationally distinguished physicians, Surgical Pathology of the Head and Neck, Second Edition now contains: over 1045 photographs, micrographs, drawings, and tables-nearly 200 more illustrations than the first edition five new chapters on molecular biology, fine-needle aspiration, vesiculobullous diseases, neck dissections, and radiation a cumulative and expanded index in each volume Unparalleled in scope and content by any other book available on the subject, Surgical Pathology of the Head and Neck, Second Edition is a must-have resource for oral, surgical, and general pathologists; otolaryngologists; oral, maxillofacial, plastic and reconstructive, general, head and neck, and orthopedic surgeons and neurosurgeons; oncologists; hematologists; ophthalmologists; radiologists; endocrinologists; dermatologists; dentists; and residents and fellows in these disciplines.

Download or read book Practical Data Science with R Second Edition written by John Mount and published by Simon and Schuster. This book was released on 2019-11-17 with total page 946 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. You’ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. About the technology Evidence-based decisions are crucial to success. Applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. The R data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively. About the book Practical Data Science with R, Second Edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the R language. By concentrating on the most important tasks you’ll face on the job, this friendly guide is comfortable both for business analysts and data scientists. Because data is only useful if it can be understood, you’ll also find fantastic tips for organizing and presenting data in tables, as well as snappy visualizations. What's inside Statistical analysis for business pros Effective data presentation The most useful R tools Interpreting complicated predictive models About the reader You’ll need to be comfortable with basic statistics and have an introductory knowledge of R or another high-level programming language. About the author Nina Zumel and John Mount founded a San Francisco–based data science consulting firm. Both hold PhDs from Carnegie Mellon University and blog on statistics, probability, and computer science.

Download or read book PMML in Action written by Alex Guazzelli and published by Createspace Independent Publishing Platform. This book was released on 2012-01-31 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: The data mining community has derived a broad foundation of statistical algorithms and software solutions that has allowed predictive analytics to become a standard approach used in science and industry. For many years, much emphasis has been placed on the development of predictive models. As a consequence, the market place offers a range of powerful tools, many open-source, for effective model building. However, once we turn to the operational deployment and practical application of predictive solutions within an existing IT infrastructure, we face a much more limited choice of options. Often it takes months for models to be integrated and deployed via custom code or proprietary processes. The Predictive Model Markup Language (PMML) standard has reached a significant stage of maturity and has obtained broad industry support, allowing users to develop predictive solutions within one application and use another to execute them. Previously, this was very difficult, but with PMML, the exchange of predictive solutions between compliant applications is now straightforward. The aim of this book is to present PMML from a practical perspective. It contains a variety of code snippets so that concepts are made clear through the use of examples. Readers are assumed to have a basic knowledge of predictive analytics and its techniques and so the book is intended for data mining movers and shakers: anyone interested in moving predictive analytic solutions between applications, including students and scientists. PMML in Action is a great way to learn how to represent your predictive solutions through a mature and refined open standard. For the 2nd edition, the book has been completely revised for PMML 4.1, the latest version of PMML. It includes new chapters and an expanded description of how to represent multiple models in PMML, including model ensemble, segmentation, chaining, and composition. The book is divided into six parts, taking you in a PMML journey in which language elements and attributes are used to represent not only modeling techniques but also data pre- and post-processing. With PMML, users benefit from a single and concise standard to represent predictive models, thus avoiding the need for custom code and proprietary solutions. You too can join the PMML movement! Unleash the power of predictive analytics and data mining today

Download or read book Nursing Informatics for the Advanced Practice Nurse Second Edition written by Susan McBride, PhD, RN-BC, CPHIMS, FAAN and published by Springer Publishing Company. This book was released on 2018-09-28 with total page 795 pages. Available in PDF, EPUB and Kindle. Book excerpt: A “must have” text for all healthcare professionals practicing in the digital age of healthcare. Nursing Informatics for the Advanced Practice Nurse, Second Edition, delivers a practical array of tools and information to show how advanced practice nurses can maximize patient safety, quality of care, and cost savings through the use of technology. Since the first edition of this text, health information technology has only expanded. With increased capability and complexity, the current technology landscape presents new challenges and opportunities for interprofessional teams. Nurses, who are already trained to use the analytic process to assess, analyze, and intervene, are in a unique position to use this same process to lead teams in addressing healthcare delivery challenges with data. The only informatics text written specifically for advanced practice nurses, Nursing Informatics for the Advanced Practice Nurse, Second Edition, takes an expansive, open, and innovative approach to thinking about technology. Every chapter is highly practical, filled with case studies and exercises that demonstrate how the content presented relates to the contemporary healthcare environment. Where applicable, concepts are aligned with the six domains within the Quality and Safety Education in Nursing (QSEN) approach and are tied to national goals and initiatives. Featuring chapters written by physicians, epidemiologists, engineers, dieticians, and health services researchers, the format of this text reflects its core principle that it takes a team to fully realize the benefit of technology for patients and healthcare consumers. What’s New Several chapters present new material to support teams’ optimization of electronic health records Updated national standards and initiatives Increased focus and new information on usability, interoperability and workflow redesign throughout, based on latest evidence Explores challenges and solutions of electronic clinical quality measures (eCQMs), a major initiative in healthcare informatics; Medicare and Medicaid Services use eCQMs to judge quality of care, and how dynamics change rapidly in today’s environment Key Features Presents national standards and healthcare initiatives Provides in-depth case studies for better understanding of informatics in practice Addresses the DNP Essentials, including II: Organization and system leadership for quality improvement and systems thinking, IV: Core Competency for Informatics, and Interprofessional Collaboration for Improving Patient and Population health outcomes Includes end-of-chapter exercises and questions for students Instructor’s Guide and PowerPoint slides for instructors Aligned with QSEN graduate-level competencies

Download or read book Machine Learning in Java written by AshishSingh Bhatia and published by Packt Publishing Ltd. This book was released on 2018-11-28 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of Java and its associated machine learning libraries to build powerful predictive models Key FeaturesSolve predictive modeling problems using the most popular machine learning Java libraries Explore data processing, machine learning, and NLP concepts using JavaML, WEKA, MALLET librariesPractical examples, tips, and tricks to help you understand applied machine learning in JavaBook Description As the amount of data in the world continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and Data Science. The main challenge is how to transform data into actionable knowledge. Machine Learning in Java will provide you with the techniques and tools you need. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. The code in this book works for JDK 8 and above, the code is tested on JDK 11. Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will have explored related web resources and technologies that will help you take your learning to the next level. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. What you will learnDiscover key Java machine learning librariesImplement concepts such as classification, regression, and clusteringDevelop a customer retention strategy by predicting likely churn candidatesBuild a scalable recommendation engine with Apache MahoutApply machine learning to fraud, anomaly, and outlier detectionExperiment with deep learning concepts and algorithmsWrite your own activity recognition model for eHealth applicationsWho this book is for If you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications with ease. You should be familiar with Java programming and some basic data mining concepts to make the most of this book, but no prior experience with machine learning is required.

Download or read book Mining Imperfect Data written by Ronald K. Pearson and published by SIAM. This book was released on 2020-09-10 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: It has been estimated that as much as 80% of the total effort in a typical data analysis project is taken up with data preparation, including reconciling and merging data from different sources, identifying and interpreting various data anomalies, and selecting and implementing appropriate treatment strategies for the anomalies that are found. This book focuses on the identification and treatment of data anomalies, including examples that highlight different types of anomalies, their potential consequences if left undetected and untreated, and options for dealing with them. As both data sources and free, open-source data analysis software environments proliferate, more people and organizations are motivated to extract useful insights and information from data of many different kinds (e.g., numerical, categorical, and text). The book emphasizes the range of open-source tools available for identifying and treating data anomalies, mostly in R but also with several examples in Python. Mining Imperfect Data: With Examples in R and Python, Second Edition presents a unified coverage of 10 different types of data anomalies (outliers, missing data, inliers, metadata errors, misalignment errors, thin levels in categorical variables, noninformative variables, duplicated records, coarsening of numerical data, and target leakage). It includes an in-depth treatment of time-series outliers and simple nonlinear digital filtering strategies for dealing with them, and it provides a detailed introduction to several useful mathematical characteristics of important data characterizations that do not appear to be widely known among practitioners, such as functional equations and key inequalities. While this book is primarily for data scientists, researchers in a variety of fields—namely statistics, machine learning, physics, engineering, medicine, social sciences, economics, and business—will also find it useful.

Download or read book Encyclopedia of Data Warehousing and Mining Second Edition written by Wang, John and published by IGI Global. This book was released on 2008-08-31 with total page 2542 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are more than one billion documents on the Web, with the count continually rising at a pace of over one million new documents per day. As information increases, the motivation and interest in data warehousing and mining research and practice remains high in organizational interest. The Encyclopedia of Data Warehousing and Mining, Second Edition, offers thorough exposure to the issues of importance in the rapidly changing field of data warehousing and mining. This essential reference source informs decision makers, problem solvers, and data mining specialists in business, academia, government, and other settings with over 300 entries on theories, methodologies, functionalities, and applications.

Download or read book Foundations of Intelligent Systems written by Jan Rauch and published by Springer Science & Business Media. This book was released on 2009-09-03 with total page 637 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 18th International Symposium on Methodologies for Intelligent Systems, ISMIS 2009, held in Prague, Czech Republic, in September 2009. The 60 revised papers presented together with 4 plenary talks were carefully reviewed and selected from over 111 submissions. The papers are organized in topical sections on knowledge discovery and data mining, applications and intelligent systems in Medicine, logical and theoretical aspects of intelligent systems, text mining, applications of intelligent sysems in music, information processing, agents, machine learning, applications of intelligent systems, complex data, general AI as well as uncertainty.

Download or read book R for Business Analytics written by A Ohri and published by Springer Science & Business Media. This book was released on 2012-09-14 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages. With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics. This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy. The book utilizes Albert Einstein’s famous remarks on making things as simple as possible, but no simpler. This book will blow the last remaining doubts in your mind about using R in your business environment. Even non-technical users will enjoy the easy-to-use examples. The interviews with creators and corporate users of R make the book very readable. The author firmly believes Isaac Asimov was a better writer in spreading science than any textbook or journal author.

Download or read book Neural Networks and Soft Computing written by Leszek Rutkowski and published by Springer Science & Business Media. This book was released on 2013-03-20 with total page 935 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents new trends and developments in soft computing techniques. Topics include: neural networks, fuzzy systems, evolutionary computation, knowledge discovery, rough sets, and hybrid methods. It also covers various applications of soft computing techniques in economics, mechanics, medicine, automatics and image processing. The book contains contributions from internationally recognized scientists, such as Zadeh, Bubnicki, Pawlak, Amari, Batyrshin, Hirota, Koczy, Kosinski, Novák, S.-Y. Lee, Pedrycz, Raudys, Setiono, Sincak, Strumillo, Takagi, Usui, Wilamowski and Zurada. An excellent overview of soft computing methods and their applications.

Download or read book Big Data Analytics Beyond Hadoop written by Vijay Srinivas Agneeswaran and published by FT Press. This book was released on 2014-05-15 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and up-to-date example code for: Spark, the next generation in-memory computing technology from UC Berkeley Storm, the parallel real-time Big Data analytics technology from Twitter GraphLab, the next-generation graph processing paradigm from CMU and the University of Washington (with comparisons to alternatives such as Pregel and Piccolo) Halo also offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data, and then realizing them in real-time. He concludes by previewing emerging trends, including real-time video analytics, SDNs, and even Big Data governance, security, and privacy issues. He identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics. Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students.

Download or read book Computer Science written by and published by PediaPress. This book was released on with total page 523 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book New Fundamental Technologies in Data Mining written by Kimito Funatsu and published by BoD – Books on Demand. This book was released on 2011-01-21 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining.

Download or read book Descendants of Nicholaus Dix Or Phillipus Gutenberger written by Roberta C. J. Dix and published by . This book was released on 1987 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book AI IA 2003 Advances in Artificial Intelligence written by Amedeo Cappelli and published by Springer. This book was released on 2003-10-24 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th Congress of the Italian Association for Artificial Intelligence, AI*IA 2003, held in Pisa, Italy in September 2003. The 44 revised full papers presented were carefully reviewed and selected from 91 submissions. The papers are organized in topical sections on knowledge representation and reasoning, soft computing, machine learning, data mining, intelligent agents, planning, robotics, natural language processing, and applications in various fields.

Download or read book Data Mining and Decision Support written by Dunja Mladenic and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining deals with finding patterns in data that are by user-definition, interesting and valid. It is an interdisciplinary area involving databases, machine learning, pattern recognition, statistics, visualization and others. Decision support focuses on developing systems to help decision-makers solve problems. Decision support provides a selection of data analysis, simulation, visualization and modeling techniques, and software tools such as decision support systems, group decision support and mediation systems, expert systems, databases and data warehouses. Independently, data mining and decision support are well-developed research areas, but until now there has been no systematic attempt to integrate them. Data Mining and Decision Support: Integration and Collaboration, written by leading researchers in the field, presents a conceptual framework, plus the methods and tools for integrating the two disciplines and for applying this technology to business problems in a collaborative setting.