LEARNING FROM DATA STREAMS

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Learning From Data Streams

Author : João Gama
ISBN : 9783540736790
Genre : Computers
File Size : 69.99 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 : 68.43 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

Knowledge Discovery From Data Streams

Author : Joao Gama
ISBN : 9781439826126
Genre : Business & Economics
File Size : 33.10 MB
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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 : 76.4 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

Autonomous Learning Systems

Author : Plamen Angelov
ISBN : 9781118481912
Genre : Science
File Size : 54.3 MB
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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

Next Generation Of Data Mining

Author : Hillol Kargupta
ISBN : 1420085875
Genre : Computers
File Size : 35.43 MB
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Drawn from the US National Science Foundation’s Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field. Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors look at how ecology, astronomy, social science, medicine, finance, and more can benefit from the next generation of data mining techniques. They examine the algorithms, middleware, infrastructure, and privacy policies associated with ubiquitous, distributed, and high performance data mining. They also discuss the impact of new technologies, such as the semantic web, on data mining and provide recommendations for privacy-preserving mechanisms. The dramatic increase in the availability of massive, complex data from various sources is creating computing, storage, communication, and human-computer interaction challenges for data mining. Providing a framework to better understand these fundamental issues, this volume surveys promising approaches to data mining problems that span an array of disciplines.
Category: Computers

Imbalanced Learning

Author : Haibo He
ISBN : 9781118646335
Genre : Technology & Engineering
File Size : 72.73 MB
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The first book of its kind to review the current status andfuture direction of the exciting new branch of machinelearning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system canlearn when it is provided with imbalanced data. Solving imbalancedlearning problems is critical in numerous data-intensive networkedsystems, including surveillance, security, Internet, finance,biomedical, defense, and more. Due to the inherent complexcharacteristics of imbalanced data sets, learning from such datarequires new understandings, principles, algorithms, and tools totransform vast amounts of raw data efficiently into information andknowledge representation. The first comprehensive look at this new branch of machinelearning, this book offers a critical review of the problem ofimbalanced learning, covering the state of the art in techniques,principles, and real-world applications. Featuring contributionsfrom experts in both academia and industry, Imbalanced Learning:Foundations, Algorithms, and Applications provides chaptercoverage on: Foundations of Imbalanced Learning Imbalanced Datasets: From Sampling to Classifiers Ensemble Methods for Class Imbalance Learning Class Imbalance Learning Methods for Support VectorMachines Class Imbalance and Active Learning Nonstationary Stream Data Learning with Imbalanced ClassDistribution Assessment Metrics for Imbalanced Learning Imbalanced Learning: Foundations, Algorithms, andApplications will help scientists and engineers learn how totackle the problem of learning from imbalanced datasets, and gaininsight into current developments in the field as well as futureresearch directions.
Category: Technology & Engineering

Data Mining Practical Machine Learning Tools And Techniques

Author : Ian H. Witten
ISBN : 9780080890364
Genre : Computers
File Size : 53.76 MB
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Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
Category: Computers

Adaptive Stream Mining

Author : Albert Bifet
ISBN : 9781607500902
Genre : Computers
File Size : 37.30 MB
Format : PDF
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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

Data Streams

Author : Charu C. Aggarwal
ISBN : 9780387475349
Genre : Computers
File Size : 86.5 MB
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This book primarily discusses issues related to the mining aspects of data streams and it is unique in its primary focus on the subject. This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions. The book is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for advanced-level students in computer science.
Category: Computers