Real Time Data Mining

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Real Time Data Mining

Author : Saed Sayad
Publisher :
ISBN 13 : 9780986606045
Total Pages : 154 pages
Book Rating : 4.6/5 (6 download)

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Book Synopsis Real Time Data Mining by : Saed Sayad

Download or read book Real Time Data Mining written by Saed Sayad and published by . This book was released on 2011-01 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining is about explaining the past and predicting the future by exploring and analyzing data. Data mining is a multi-disciplinary field which combines statistics, machine learning, artificial intelligence and database technology.Although data mining algorithms are widely used in extremely diverse situations, in practice, one or more major limitations almost invariably appear and significantly constrain successful data mining applications. Frequently, these problems are associated with large increases in the rate of generation of data, the quantity of data and the number of attributes (variables) to be processed: Increasingly, the data situation is now beyond the capabilities of conventional data mining methods.The term Real Time is used to describe how well a data mining algorithm can accommodate an ever increasing data load instantaneously. Upgrading conventional data mining to real time data mining is through the use of a method termed the Real Time Learning Machine or RTLM. The use of the RTLM with conventional data mining methods enables Real Time Data Mining.The future of predictive modeling belongs to real time data mining and the main motivation in authoring this book is to help you to understand the method and to implement it for your applications.

Realtime Data Mining

Author : Alexander Paprotny
Publisher : Birkhäuser
ISBN 13 : 9783319344454
Total Pages : 0 pages
Book Rating : 4.3/5 (444 download)

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Book Synopsis Realtime Data Mining by : Alexander Paprotny

Download or read book Realtime Data Mining written by Alexander Paprotny and published by Birkhäuser. This book was released on 2016-08-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore, it presents promising results of numerous experiments on real-world data. The area of realtime data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today's “classic” data mining systems. Whereas the latter learn from historical data and then use it to deduce necessary actions, realtime analytics systems learn and act continuously and autonomously. In the vanguard of these new analytics systems are recommendation engines. They are principally found on the Internet, where all information is available in realtime and an immediate feedback is guaranteed. This monograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinking by considering recommendation tasks as control-theoretic problems. Realtime Data Mining: Self-Learning Techniques for Recommendation Engines will also interest application-oriented mathematicians because it consistently combines some of the most promising mathematical areas, namely control theory, multilevel approximation, and tensor factorization.

Machine Learning for Data Streams

Author : Albert Bifet
Publisher : MIT Press
ISBN 13 : 0262346052
Total Pages : 262 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Machine Learning for Data Streams by : Albert Bifet

Download or read book Machine Learning for Data Streams written by Albert Bifet and published by MIT Press. This book was released on 2018-03-16 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.

Real-Time Analytics

Author : Byron Ellis
Publisher : John Wiley & Sons
ISBN 13 : 1118838025
Total Pages : 432 pages
Book Rating : 4.1/5 (188 download)

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Book Synopsis Real-Time Analytics by : Byron Ellis

Download or read book Real-Time Analytics written by Byron Ellis and published by John Wiley & Sons. This book was released on 2014-06-23 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Construct a robust end-to-end solution for analyzing and visualizing streaming data Real-time analytics is the hottest topic in data analytics today. In Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, expert Byron Ellis teaches data analysts technologies to build an effective real-time analytics platform. This platform can then be used to make sense of the constantly changing data that is beginning to outpace traditional batch-based analysis platforms. The author is among a very few leading experts in the field. He has a prestigious background in research, development, analytics, real-time visualization, and Big Data streaming and is uniquely qualified to help you explore this revolutionary field. Moving from a description of the overall analytic architecture of real-time analytics to using specific tools to obtain targeted results, Real-Time Analytics leverages open source and modern commercial tools to construct robust, efficient systems that can provide real-time analysis in a cost-effective manner. The book includes: A deep discussion of streaming data systems and architectures Instructions for analyzing, storing, and delivering streaming data Tips on aggregating data and working with sets Information on data warehousing options and techniques Real-Time Analytics includes in-depth case studies for website analytics, Big Data, visualizing streaming and mobile data, and mining and visualizing operational data flows. The book's "recipe" layout lets readers quickly learn and implement different techniques. All of the code examples presented in the book, along with their related data sets, are available on the companion website.

Data Mining Applications with R

Author : Yanchang Zhao
Publisher : Academic Press
ISBN 13 : 0124115209
Total Pages : 493 pages
Book Rating : 4.1/5 (241 download)

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Book Synopsis Data Mining Applications with R by : Yanchang Zhao

Download or read book Data Mining Applications with R written by Yanchang Zhao and published by Academic Press. This book was released on 2013-11-26 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool. R code, Data and color figures for the book are provided at the RDataMining.com website. - Helps data miners to learn to use R in their specific area of work and see how R can apply in different industries - Presents various case studies in real-world applications, which will help readers to apply the techniques in their work - Provides code examples and sample data for readers to easily learn the techniques by running the code by themselves

Realtime Data Mining

Author : Alexander Paprotny
Publisher : Springer Science & Business Media
ISBN 13 : 3319013211
Total Pages : 333 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis Realtime Data Mining by : Alexander Paprotny

Download or read book Realtime Data Mining written by Alexander Paprotny and published by Springer Science & Business Media. This book was released on 2013-12-03 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore, it presents promising results of numerous experiments on real-world data. The area of realtime data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today's “classic” data mining systems. Whereas the latter learn from historical data and then use it to deduce necessary actions, realtime analytics systems learn and act continuously and autonomously. In the vanguard of these new analytics systems are recommendation engines. They are principally found on the Internet, where all information is available in realtime and an immediate feedback is guaranteed. This monograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinking by considering recommendation tasks as control-theoretic problems. Realtime Data Mining: Self-Learning Techniques for Recommendation Engines will also interest application-oriented mathematicians because it consistently combines some of the most promising mathematical areas, namely control theory, multilevel approximation, and tensor factorization.

Data Mining Solutions

Author : Christopher Westphal
Publisher :
ISBN 13 :
Total Pages : 648 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Data Mining Solutions by : Christopher Westphal

Download or read book Data Mining Solutions written by Christopher Westphal and published by . This book was released on 1998-08-10 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cutting-edge data mining techniques and tools for solving your toughest analytical problems Data Mining Solutions In down-to-earth language, data mining experts Christopher Westphal and Teresa Blaxton introduce a brand new approach to data mining analysis. Through their extensive real-world experience, they have developed and documented many practical and proven techniques to make your own data mining efforts more successful. You'll get a refreshing "out-of-the-box" approach to data mining that will help you maximize your time and problem-solving resources, and prepare for the next wave of data mining-visualization. You will read about ways in which data mining has been used to: * Discover patterns of insider trading in the stock market * Evaluate the utility of marketing campaigns * Analyze retail sales patterns across geographic regions * Identify money laundering operations * Target DNA sequences for pharmaceutical testing and development The book is accompanied by a CD-ROM that contains: * Demo and trial versions of numerous visual data mining tools * Active web-page links for each of the products profiled * GIF files corresponding to all book images

Big Data

Author : James Warren
Publisher : Simon and Schuster
ISBN 13 : 1638351104
Total Pages : 481 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Big Data by : James Warren

Download or read book Big Data written by James Warren and published by Simon and Schuster. This book was released on 2015-04-29 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing. Table of Contents A new paradigm for Big Data PART 1 BATCH LAYER Data model for Big Data Data model for Big Data: Illustration Data storage on the batch layer Data storage on the batch layer: Illustration Batch layer Batch layer: Illustration An example batch layer: Architecture and algorithms An example batch layer: Implementation PART 2 SERVING LAYER Serving layer Serving layer: Illustration PART 3 SPEED LAYER Realtime views Realtime views: Illustration Queuing and stream processing Queuing and stream processing: Illustration Micro-batch stream processing Micro-batch stream processing: Illustration Lambda Architecture in depth

Machine Learning and Data Mining

Author : Igor Kononenko
Publisher : Horwood Publishing
ISBN 13 : 9781904275213
Total Pages : 484 pages
Book Rating : 4.2/5 (752 download)

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Book Synopsis Machine Learning and Data Mining by : Igor Kononenko

Download or read book Machine Learning and Data Mining written by Igor Kononenko and published by Horwood Publishing. This book was released on 2007-04-30 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. Written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining, this text is suitable foradvanced undergraduates, postgraduates and tutors in a wide area of computer science and technology, as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to libraries and bookshelves of the many companies who are using the principles of data mining to effectively deliver solid business and industry solutions.

Data Mining in Time Series Databases

Author : Abraham Kandel
Publisher : World Scientific
ISBN 13 : 981256540X
Total Pages : 205 pages
Book Rating : 4.8/5 (125 download)

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Book Synopsis Data Mining in Time Series Databases by : Abraham Kandel

Download or read book Data Mining in Time Series Databases written by Abraham Kandel and published by World Scientific. This book was released on 2004 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery. This book covers the state-of-the-artmethodology for mining time series databases. The novel data miningmethods presented in the book include techniques for efficientsegmentation, indexing, and classification of noisy and dynamic timeseries. A graph-based method for anomaly detection in time series isdescribed and the book also studies the implications of a novel andpotentially useful representation of time series as strings. Theproblem of detecting changes in data mining models that are inducedfrom temporal databases is additionally discussed.

Frontiers in Massive Data Analysis

Author : National Research Council
Publisher : National Academies Press
ISBN 13 : 0309287812
Total Pages : 191 pages
Book Rating : 4.3/5 (92 download)

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Book Synopsis Frontiers in Massive Data Analysis by : National Research Council

Download or read book Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-09-03 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.

Data Mining and Machine Learning Applications

Author : Rohit Raja
Publisher : John Wiley & Sons
ISBN 13 : 1119791782
Total Pages : 500 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Data Mining and Machine Learning Applications by : Rohit Raja

Download or read book Data Mining and Machine Learning Applications written by Rohit Raja and published by John Wiley & Sons. This book was released on 2022-03-02 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

Introduction to Data Mining and Its Applications

Author : S. Sumathi
Publisher : Springer Science & Business Media
ISBN 13 : 3540343504
Total Pages : 836 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Introduction to Data Mining and Its Applications by : S. Sumathi

Download or read book Introduction to Data Mining and Its Applications written by S. Sumathi and published by Springer Science & Business Media. This book was released on 2006-09-26 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. This book is intended for a wide audience of readers who are not necessarily experts in data warehousing and data mining, but are interested in receiving a general introduction to these areas and their many practical applications. Since data mining technology has become a hot topic not only among academic students but also for decision makers, it provides valuable hidden business and scientific intelligence from a large amount of historical data. It is also written for technical managers and executives as well as for technologists interested in learning about data mining.

Data Mining Tools for Malware Detection

Author : Mehedy Masud
Publisher : CRC Press
ISBN 13 : 1439854548
Total Pages : 453 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Data Mining Tools for Malware Detection by : Mehedy Masud

Download or read book Data Mining Tools for Malware Detection written by Mehedy Masud and published by CRC Press. This book was released on 2011-12-07 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the use of data mining for security and malware detection is quickly on the rise, most books on the subject provide high-level theoretical discussions to the near exclusion of the practical aspects. Breaking the mold, Data Mining Tools for Malware Detection provides a step-by-step breakdown of how to develop data mining tools for malware detection. Integrating theory with practical techniques and experimental results, it focuses on malware detection applications for email worms, malicious code, remote exploits, and botnets. The authors describe the systems they have designed and developed: email worm detection using data mining, a scalable multi-level feature extraction technique to detect malicious executables, detecting remote exploits using data mining, and flow-based identification of botnet traffic by mining multiple log files. For each of these tools, they detail the system architecture, algorithms, performance results, and limitations. Discusses data mining for emerging applications, including adaptable malware detection, insider threat detection, firewall policy analysis, and real-time data mining Includes four appendices that provide a firm foundation in data management, secure systems, and the semantic web Describes the authors’ tools for stream data mining From algorithms to experimental results, this is one of the few books that will be equally valuable to those in industry, government, and academia. It will help technologists decide which tools to select for specific applications, managers will learn how to determine whether or not to proceed with a data mining project, and developers will find innovative alternative designs for a range of applications.

The Top Ten Algorithms in Data Mining

Author : Xindong Wu
Publisher : CRC Press
ISBN 13 : 142008965X
Total Pages : 230 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis The Top Ten Algorithms in Data Mining by : Xindong Wu

Download or read book The Top Ten Algorithms in Data Mining written by Xindong Wu and published by CRC Press. This book was released on 2009-04-09 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is wri

Data Mining and Big Data

Author : Ying Tan
Publisher : Springer
ISBN 13 : 3319409735
Total Pages : 564 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Data Mining and Big Data by : Ying Tan

Download or read book Data Mining and Big Data written by Ying Tan and published by Springer. This book was released on 2016-07-04 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: The LNCS volume LNCS 9714 constitutes the refereed proceedings of the International Conference on Data Mining and Big Data, DMBD 2016, held in Bali, Indonesia, in June 2016. The 57 papers presented in this volume were carefully reviewed and selected from 115 submissions. The theme of DMBD 2016 is "Serving Life with Data Science". Data mining refers to the activity of going through big data sets to look for relevant or pertinent information.The papers are organized in 10 cohesive sections covering all major topics of the research and development of data mining and big data and one Workshop on Computational Aspects of Pattern Recognition and Computer Vision.

New Fundamental Technologies in Data Mining

Author : Kimito Funatsu
Publisher : BoD – Books on Demand
ISBN 13 : 9533075473
Total Pages : 600 pages
Book Rating : 4.5/5 (33 download)

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Book Synopsis New Fundamental Technologies in Data Mining by : Kimito Funatsu

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.