Analysis of Data and Processes: From Standard to Realtime Data Mining PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Analysis of Data and Processes: From Standard to Realtime Data Mining PDF full book. Access full book title Analysis of Data and Processes: From Standard to Realtime Data Mining by Armenak Barsegian. Download full books in PDF and EPUB format.
Author: Armenak Barsegian
Publisher:
ISBN: 9783868705935
Category :
Languages : en
Pages : 300
Get Book
Book Description
Author: Armenak Barsegian
Publisher:
ISBN: 9783868705935
Category :
Languages : en
Pages : 300
Get Book
Book Description
Author: Mark F. Hornick
Publisher: Elsevier
ISBN: 9780080495910
Category : Computers
Languages : en
Pages : 544
Get Book
Book Description
Whether you are a software developer, systems architect, data analyst, or business analyst, if you want to take advantage of data mining in the development of advanced analytic applications, Java Data Mining, JDM, the new standard now implemented in core DBMS and data mining/analysis software, is a key solution component. This book is the essential guide to the usage of the JDM standard interface, written by contributors to the JDM standard. Data mining introduction - an overview of data mining and the problems it can address across industries; JDM's place in strategic solutions to data mining-related problems JDM essentials - concepts, design approach and design issues, with detailed code examples in Java; a Web Services interface to enable JDM functionality in an SOA environment; and illustration of JDM XML Schema for JDM objects JDM in practice - the use of JDM from vendor implementations and approaches to customer applications, integration, and usage; impact of data mining on IT infrastructure; a how-to guide for building applications that use the JDM API Free, downloadable KJDM source code referenced in the book available here
Author: Jan vom Brocke
Publisher: Springer
ISBN: 3319583077
Category : Business & Economics
Languages : en
Pages : 610
Get Book
Book Description
This book is the first to present a rich selection of over 30 real-world cases of how leading organizations conduct Business Process Management (BPM). The cases stem from a diverse set of industry sectors and countries on different continents, reporting on best practices and lessons learned. The book showcases how BPM can contribute to both exploitation and exploration in a digital world. All cases are presented using a uniform structure in order to provide valuable insights and essential guidance for students and practitioners.
Author: Galit Shmueli
Publisher: John Wiley & Sons
ISBN: 1118729242
Category : Mathematics
Languages : en
Pages : 563
Get Book
Book Description
An applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes: Real-world examples to build a theoretical and practical understanding of key data mining methods End-of-chapter exercises that help readers better understand the presented material Data-rich case studies to illustrate various applications of data mining techniques Completely new chapters on social network analysis and text mining A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides https://www.dataminingbook.com Free 140-day license to use XLMiner for Education software Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology. Praise for the Second Edition "...full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing."– Research Magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature." – ComputingReviews.com "Excellent choice for business analysts...The book is a perfect fit for its intended audience." – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks and book chapters. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad for 15 years.
Author: Raghvendra Kumar
Publisher: Springer Nature
ISBN: 9811579652
Category : Technology & Engineering
Languages : en
Pages : 216
Get Book
Book Description
This book provides theoretical and practical approach in the area of multimedia and IOT applications and performance analysis. Further, multimedia communication, deep learning models to multimedia data and the new (IOT) approaches are also covered. It addresses the complete functional framework in the area of multimedia data, IOT and smart computing techniques. The book proposes a comprehensive overview of the state-of-the-art research work on multimedia analysis in IOT applications. It bridges the gap between multimedia concepts and solutions by providing the current IOT frameworks, their applications in multimedia analysis, the strengths and limitations of the existing methods, and the future directions in multimedia IOT analytics.
Author: Narayan Changder
Publisher: CHANGDER OUTLINE
ISBN:
Category : Computers
Languages : en
Pages : 279
Get Book
Book Description
Unlock the potential of data with our groundbreaking MCQ book, "Mastering Data Mining." Whether you're a seasoned data professional, a student entering the world of analytics, or a curious mind eager to delve into the realm of data, this comprehensive guide is your key to mastering the art and science of data mining. Key Features: In-Depth Exploration: Dive deep into the core concepts of data mining, from fundamental principles to advanced techniques. This book is your gateway to understanding how to extract valuable insights and patterns from vast datasets, equipping you with the skills to make informed decisions in today's data-driven world. Practical Applications: Move beyond theory with real-world applications of data mining. The book includes practical examples and case studies that demonstrate how data mining techniques are applied across various industries, giving you a hands-on understanding of their significance and impact. MCQs for Mastery: Reinforce your learning with a plethora of thoughtfully designed multiple-choice questions. Each question is crafted to test your understanding of key concepts, ensuring that you not only grasp theoretical knowledge but can also apply it effectively. Algorithmic Insights: Gain a solid foundation in the algorithms that power data mining. From clustering and association rule mining to classification and regression, this book demystifies complex algorithms, making them accessible to both beginners and experienced practitioners. Data Preprocessing Techniques: Understand the importance of data preprocessing and learn how to clean, transform, and prepare data for mining. This crucial step is often overlooked, but our book places a strong emphasis on ensuring your data is primed for meaningful analysis. Keyword Mastery: Navigate the landscape of data mining with ease using strategically placed keywords. This ensures that you not only comprehend the intricacies of data mining but also familiarize yourself with the terminology commonly used in the field. Practical Guidance: Beyond theory and algorithms, "Mastering Data Mining" provides practical guidance on selecting the right tools, interpreting results, and making informed decisions based on data-driven insights. This holistic approach prepares you for success in real-world scenarios. Who Will Benefit: Data Scientists and Analysts Students Pursuing Data Science Courses Business Intelligence Professionals Researchers and Academicians Anyone Intrigued by the Power of Data Elevate your data mining skills and embark on a journey of discovery. "Mastering Data Mining" is not just a book; it's your comprehensive guide to navigating the intricacies of data, transforming raw information into actionable intelligence. Order now and unlock the doors to a world of opportunities in the dynamic field of data mining. Transform data into knowledge. Master data mining with confidence and competence.
Author: Daniel T. Larose
Publisher: John Wiley & Sons
ISBN: 1118873572
Category : Computers
Languages : en
Pages : 336
Get Book
Book Description
The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book
Author: Saumyadipta Pyne
Publisher: Springer
ISBN: 8132236289
Category : Computers
Languages : en
Pages : 276
Get Book
Book Description
This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.
Author: Peter Ghavami
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 1547401567
Category : Business & Economics
Languages : en
Pages : 254
Get Book
Book Description
Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.
Author: Ramjan, Sarawut
Publisher: IGI Global
ISBN: 1668447320
Category : Computers
Languages : en
Pages : 326
Get Book
Book Description
The demand for skilled data scientists is rapidly increasing as more organizations recognize the value of data-driven decision- making. Data science, data management, and data mining are all critical components for various types of organizations, including large and small corporations, academic institutions, and government entities. For companies, these components serve to extract insights and value from their data, empowering them to make evidence-driven decisions and gain a competitive advantage by discovering patterns and trends and avoiding costly mistakes. Academic institutions utilize these tools to analyze large datasets and gain insights into various scientific fields of study, including genetic data, climate data, financial data, and in the social sciences they are used to analyze survey data, behavioral data, and public opinion data. Governments use data science to analyze data that can inform policy decisions, such as identifying areas with high crime rates, determining which regions need infrastructure development, and predicting disease outbreaks. However, individuals who are not data science experts, but are experts within their own fields, may need to apply their experience to the data they must manage, but still struggle to expand their knowledge of how to use data mining tools such as RapidMiner software. Principles and Theories of Data Mining With RapidMiner is a comprehensive guide for students and individuals interested in experimenting with data mining using RapidMiner software. This book takes a practical approach to learning through the RapidMiner tool, with exercises and case studies that demonstrate how to apply data mining techniques to real-world scenarios. Readers will learn essential concepts related to data mining, such as supervised learning, unsupervised learning, association rule mining, categorical data, continuous data, and data quality. Additionally, readers will learn how to apply data mining techniques to popular algorithms, including k-nearest neighbor (K-NN), decision tree, naïve bayes, artificial neural network (ANN), k-means clustering, and probabilistic methods. By the end of the book, readers will have the skills and confidence to use RapidMiner software effectively and efficiently, making it an ideal resource for anyone, whether a student or a professional, who needs to expand their knowledge of data mining with RapidMiner software.
