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Learning From Data Streams

Author : João Gama
ISBN : 9783540736790
Genre : Computers
File Size : 71.42 MB
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Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. Applications in security, the natural sciences, and education are presented. The huge bibliography offers an excellent starting point for further reading and future research.
Category: Computers

Learning From Data Streams In Evolving Environments

Author : Moamar Sayed-Mouchaweh
ISBN : 9783319898032
Genre : Technology & Engineering
File Size : 72.35 MB
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This edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by evolving non-stationary processes. The goal is to discuss and overview the advanced techniques, methods and tools that are dedicated to manage, exploit and interpret data streams in non-stationary environments. The book includes the required notions, definitions, and background to understand the problem of learning from data streams in non-stationary environments and synthesizes the state-of-the-art in the domain, discussing advanced aspects and concepts and presenting open problems and future challenges in this field. Provides multiple examples to facilitate the understanding data streams in non-stationary environments; Presents several application cases to show how the methods solve different real world problems; Discusses the links between methods to help stimulate new research and application directions.
Category: Technology & Engineering

Machine Learning For Data Streams

Author : Albert Bifet
ISBN : 0262037793
Genre : Computers
File Size : 63.42 MB
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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.
Category: Computers

Knowledge Discovery From Data Streams

Author : Joao Gama
ISBN : 9781439826126
Genre : Business & Economics
File Size : 90.2 MB
Format : PDF
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Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents a coherent overview of state-of-the-art research in learning from data streams. The book covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks, and customer click streams. It also addresses several challenges of data mining in the future, when stream mining will be at the core of many applications. These challenges involve designing useful and efficient data mining solutions applicable to real-world problems. In the appendix, the author includes examples of publicly available software and online data sets. This practical, up-to-date book focuses on the new requirements of the next generation of data mining. Although the concepts presented in the text are mainly about data streams, they also are valid for different areas of machine learning and data mining.
Category: Business & Economics

Learning From Data Streams In Dynamic Environments

Author : Moamar Sayed-Mouchaweh
ISBN : 9783319256672
Genre : Computers
File Size : 71.87 MB
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This book addresses the problems of modeling, prediction, classification, data understanding and processing in non-stationary and unpredictable environments. It presents major and well-known methods and approaches for the design of systems able to learn and to fully adapt its structure and to adjust its parameters according to the changes in their environments. Also presents the problem of learning in non-stationary environments, its interests, its applications and challenges and studies the complementarities and the links between the different methods and techniques of learning in evolving and non-stationary environments.
Category: Computers

Adaptive Stream Mining

Author : Albert Bifet
ISBN : 9781607500902
Genre : Computers
File Size : 65.1 MB
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This book is a significant contribution to the subject of mining time-changing data streams and addresses the design of learning algorithms for this purpose. It introduces new contributions on several different aspects of the problem, identifying research opportunities and increasing the scope for applications. It also includes an in-depth study of stream mining and a theoretical analysis of proposed methods and algorithms. The first section is concerned with the use of an adaptive sliding window algorithm (ADWIN). Since this has rigorous performance guarantees, using it in place of counters or accumulators, it offers the possibility of extending such guarantees to learning and mining algorithms not initially designed for drifting data. Testing with several methods, including Naive Bayes, clustering, decision trees and ensemble methods, is discussed as well. The second part of the book describes a formal study of connected acyclic graphs, or 'trees', from the point of view of closure-based mining, presenting efficient algorithms for subtree testing and for mining ordered and unordered frequent closed trees. Lastly, a general methodology to identify closed patterns in a data stream is outlined. This is applied to develop an incremental method, a sliding-window based method, and a method that mines closed trees adaptively from data streams. These are used to introduce classification methods for tree data streams."
Category: Computers

Autonomous Learning Systems

Author : Plamen Angelov
ISBN : 9781118481912
Genre : Science
File Size : 46.6 MB
Format : PDF, Docs
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Autonomous Learning Systems is the result of over adecade of focused research and studies in this emerging area whichspans a number of well-known and well-established disciplines thatinclude machine learning, system identification, data mining, fuzzylogic, neural networks, neuro-fuzzy systems, control theory andpattern recognition. The evolution of these systems has been bothindustry-driven with an increasing demand from sectors such asdefence and security, aerospace and advanced process industries,bio-medicine and intelligent transportation, as well asresearch-driven – there is a strong trend of innovation ofall of the above well-established research disciplines that islinked to their on-line and real-time application; theiradaptability and flexibility. Providing an introduction to the key technologies, detailedtechnical explanations of the methodology, and an illustration ofthe practical relevance of the approach with a wide range ofapplications, this book addresses the challenges of autonomouslearning systems with a systematic approach that lays thefoundations for a fast growing area of research that will underpina range of technological applications vital to both industry andsociety. Key features: Presents the subject systematically from explaining thefundamentals to illustrating the proposed approach with numerousapplications. Covers a wide range of applications in fields includingunmanned vehicles/robotics, oil refineries, chemical industry,evolving user behaviour and activity recognition. Reviews traditional fields including clustering,classification, control, fault detection and anomalydetection, filtering and estimation through the prism of evolvingand autonomously learning mechanisms. Accompanied by a website hosting additional material, includingthe software toolbox and lecture notes. Autonomous Learning Systems provides a ‘one-stopshop’ on the subject for academics, students, researchers andpracticing engineers. It is also a valuable reference forGovernment agencies and software developers.
Category: Science

Advances In Machine Learning And Data Mining For Astronomy

Author : Michael J. Way
ISBN : 9781439841730
Genre : Computers
File Size : 22.17 MB
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Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.
Category: Computers

Machine Learning And Data Mining In Pattern Recognition

Author : Petra Perner
ISBN : 9783642030703
Genre : Computers
File Size : 60.99 MB
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There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.
Category: Computers

Data Mining

Author : Ian H. Witten
ISBN : 3446215336
Genre :
File Size : 69.13 MB
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