Stream Data Mining Algorithms And Their Probabilistic Properties

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Stream Data Mining: Algorithms and Their Probabilistic Properties

Leszek Rutkowski,Maciej Jaworski,Piotr Duda
Stream Data Mining Algorithms and Their Probabilistic Properties Book PDF

Author : Leszek Rutkowski,Maciej Jaworski,Piotr Duda
Publisher : Springer
Page : 330 pages
File Size : 53,7 Mb
Release : 2019-03-16
Category : Technology & Engineering
ISBN : 9783030139629

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Stream Data Mining: Algorithms and Their Probabilistic Properties by Leszek Rutkowski,Maciej Jaworski,Piotr Duda Pdf

This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who deal with stream data, e.g. in telecommunication, banking, and sensor networks.

Adaptive Stream Mining

Albert Bifet
Adaptive Stream Mining Book PDF

Author : Albert Bifet
Publisher : IOS Press
Page : 224 pages
File Size : 48,7 Mb
Release : 2010
Category : Computers
ISBN : 9781607500902

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Adaptive Stream Mining by Albert Bifet Pdf

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 Naïve 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.

Artificial Intelligence and Soft Computing

Leszek Rutkowski,Rafał Scherer,Marcin Korytkowski,Witold Pedrycz,Ryszard Tadeusiewicz,Jacek M. Zurada
Artificial Intelligence and Soft Computing Book PDF

Author : Leszek Rutkowski,Rafał Scherer,Marcin Korytkowski,Witold Pedrycz,Ryszard Tadeusiewicz,Jacek M. Zurada
Publisher : Springer
Page : 712 pages
File Size : 49,6 Mb
Release : 2019-05-27
Category : Computers
ISBN : 9783030209155

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Artificial Intelligence and Soft Computing by Leszek Rutkowski,Rafał Scherer,Marcin Korytkowski,Witold Pedrycz,Ryszard Tadeusiewicz,Jacek M. Zurada Pdf

The two-volume set LNCS 11508 and 11509 constitutes the refereed proceedings of of the 18th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2019, held in Zakopane, Poland, in June 2019. The 122 revised full papers presented were carefully reviewed and selected from 333 submissions. The papers included in the first volume are organized in the following five parts: neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; pattern classification; artificial intelligence in modeling and simulation. The papers included in the second volume are organized in the following five parts: computer vision, image and speech analysis; bioinformatics, biometrics, and medical applications; data mining; various problems of artificial intelligence; agent systems, robotics and control.

Data Streams

Charu C. Aggarwal
Data Streams Book PDF

Author : Charu C. Aggarwal
Publisher : Springer Science & Business Media
Page : 365 pages
File Size : 42,7 Mb
Release : 2007-04-03
Category : Computers
ISBN : 9780387475349

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Data Streams by Charu C. Aggarwal Pdf

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.

Data Mining In Time Series And Streaming Databases

Last Mark,Kandel Abraham,Bunke Horst
Data Mining In Time Series And Streaming Databases Book PDF

Author : Last Mark,Kandel Abraham,Bunke Horst
Publisher : World Scientific
Page : 196 pages
File Size : 49,8 Mb
Release : 2018-01-11
Category : Computers
ISBN : 9789813228054

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Data Mining In Time Series And Streaming Databases by Last Mark,Kandel Abraham,Bunke Horst Pdf

This compendium is a completely revised version of an earlier book, Data Mining in Time Series Databases, by the same editors. It provides a unique collection of new articles written by leading experts that account for the latest developments in the field of time series and data stream mining. The emerging topics covered by the book include weightless neural modeling for mining data streams, using ensemble classifiers for imbalanced and evolving data streams, document stream mining with active learning, and many more. In particular, it addresses the domain of streaming data, which has recently become one of the emerging topics in Data Science, Big Data, and related areas. Existing titles do not provide sufficient information on this topic. Contents: Streaming Data Mining with Massive Online Analytics (MOA) (Albert Bifet, Jesse Read, Geoff Holmes and Bernhard Pfahringer)Weightless Neural Modeling for Mining Data Streams (Douglas O Cardoso, João Gama and Felipe França)Ensemble Classifiers for Imbalanced and Evolving Data Streams (Dariusz Brzezinski and Jerzy Stefanowski)Consensus Learning for Sequence Data (Andreas Nienkötter and Xiaoyi Jiang)Clustering-Based Classification of Document Streams with Active Learning (Mark Last, Maxim Stoliar and Menahem Friedman)Supporting the Mining of Big Data by Means of Domain Knowledge During the Pre-mining Phases (Rémon Cornelisse and Sunil Choenni)Data Analytics: Industrial Perspective & Solutions for Streaming Data (Mohsin Munir, Sebastian Baumbach, Ying Gu, Andreas Dengel and Sheraz Ahmed) Readership: Researchers, academics, professionals and graduate students in artificial intelligence, machine learning, databases, and information science. Keywords: Time Series;Data Streams;Big Data;Internet of Things;Concept Drift;Sequence Mining;Episode Mining;Incremental Learning;Active LearningReview:0

Neural Information Processing

Tom Gedeon,Kok Wai Wong,Minho Lee
Neural Information Processing Book PDF

Author : Tom Gedeon,Kok Wai Wong,Minho Lee
Publisher : Springer Nature
Page : 802 pages
File Size : 42,7 Mb
Release : 2019-12-05
Category : Computers
ISBN : 9783030368029

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Neural Information Processing by Tom Gedeon,Kok Wai Wong,Minho Lee Pdf

The two-volume set CCIS 1142 and 1143 constitutes thoroughly refereed contributions presented at the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019. For ICONIP 2019 a total of 345 papers was carefully reviewed and selected for publication out of 645 submissions. The 168 papers included in this volume set were organized in topical sections as follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred computing; human centred computing and medicine; human centred computing for emotion; hybrid models; image processing by neural techniques; learning from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based approaches; social network computing; spiking neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models.

Cloud Computing, Big Data & Emerging Topics

Enzo Rucci,Marcelo Naiouf,Franco Chichizola,Laura De Giusti,Armando De Giusti
Cloud Computing Big Data Emerging Topics Book PDF

Author : Enzo Rucci,Marcelo Naiouf,Franco Chichizola,Laura De Giusti,Armando De Giusti
Publisher : Springer Nature
Page : 146 pages
File Size : 55,6 Mb
Release : 2022-08-04
Category : Computers
ISBN : 9783031145995

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Cloud Computing, Big Data & Emerging Topics by Enzo Rucci,Marcelo Naiouf,Franco Chichizola,Laura De Giusti,Armando De Giusti Pdf

This book constitutes the revised selected papers of the 10th International Conference on Cloud Computing, Big Data & Emerging Topics, JCC-BD&ET 2022, held in La Plata, Argentina*, in June-July 2022. The 9 full papers were carefully reviewed and selected from a total of 23 submissions. The papers are organized in topical sections on: Parallel and Distributed Computing; Machine and Deep Learning; Cloud and High-Performance Computing, Machine and Deep Learning, and Virtual Reality.

Data Stream Management

Minos Garofalakis,Johannes Gehrke,Rajeev Rastogi
Data Stream Management Book PDF

Author : Minos Garofalakis,Johannes Gehrke,Rajeev Rastogi
Publisher : Springer
Page : 537 pages
File Size : 50,5 Mb
Release : 2016-07-11
Category : Computers
ISBN : 9783540286080

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Data Stream Management by Minos Garofalakis,Johannes Gehrke,Rajeev Rastogi Pdf

This volume focuses on the theory and practice of data stream management, and the novel challenges this emerging domain poses for data-management algorithms, systems, and applications. The collection of chapters, contributed by authorities in the field, offers a comprehensive introduction to both the algorithmic/theoretical foundations of data streams, as well as the streaming systems and applications built in different domains. A short introductory chapter provides a brief summary of some basic data streaming concepts and models, and discusses the key elements of a generic stream query processing architecture. Subsequently, Part I focuses on basic streaming algorithms for some key analytics functions (e.g., quantiles, norms, join aggregates, heavy hitters) over streaming data. Part II then examines important techniques for basic stream mining tasks (e.g., clustering, classification, frequent itemsets). Part III discusses a number of advanced topics on stream processing algorithms, and Part IV focuses on system and language aspects of data stream processing with surveys of influential system prototypes and language designs. Part V then presents some representative applications of streaming techniques in different domains (e.g., network management, financial analytics). Finally, the volume concludes with an overview of current data streaming products and new application domains (e.g. cloud computing, big data analytics, and complex event processing), and a discussion of future directions in this exciting field. The book provides a comprehensive overview of core concepts and technological foundations, as well as various systems and applications, and is of particular interest to students, lecturers and researchers in the area of data stream management.

Principles of Data Mining

Max Bramer
Principles of Data Mining Book PDF

Author : Max Bramer
Publisher : Springer
Page : 526 pages
File Size : 46,8 Mb
Release : 2016-11-09
Category : Computers
ISBN : 9781447173076

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Principles of Data Mining by Max Bramer Pdf

This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.

Connected e-Health

Sushruta Mishra,Alfonso González-Briones,Akash Kumar Bhoi,Pradeep Kumar Mallick,Juan M. Corchado
Connected e Health Book PDF

Author : Sushruta Mishra,Alfonso González-Briones,Akash Kumar Bhoi,Pradeep Kumar Mallick,Juan M. Corchado
Publisher : Springer Nature
Page : 487 pages
File Size : 46,5 Mb
Release : 2022-05-05
Category : Technology & Engineering
ISBN : 9783030979294

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Connected e-Health by Sushruta Mishra,Alfonso González-Briones,Akash Kumar Bhoi,Pradeep Kumar Mallick,Juan M. Corchado Pdf

With rise of smart medical sensors, cloud computing and the health care technologies, “connected health” is getting remarkable consideration everywhere. Recently, the Internet of Things (IoT) has brought the vision of a smarter world into reality. Cloud computing fits well in this scenario as it can provide high quality of clinical experience. Thus an IoT-cloud convergence can play a vital role in healthcare by offering better insight of heterogeneous healthcare content supporting quality care. It can also support powerful processing and storage facilities of huge data to provide automated decision making. This book aims to report quality research on recent advances towards IoT-Cloud convergence for smart healthcare, more specifically to the state-of-the-art approaches, design, development and innovative use of those convergence methods for providing insights into healthcare service demands. Students, researchers, and medical experts in the field of information technology, medicine, cloud computing, soft computing technologies, IoT and the related fields can benefit from this handbook in handling real-time challenges in healthcare. Current books are limited to focus either on soft computing algorithms or smart healthcare. Integration of smart and cloud computing models in healthcare resulting in connected health is explored in detail in this book.

Machine Learning for Data Streams

Albert Bifet,Ricard Gavalda,Geoffrey Holmes,Bernhard Pfahringer
Machine Learning for Data Streams Book PDF

Author : Albert Bifet,Ricard Gavalda,Geoffrey Holmes,Bernhard Pfahringer
Publisher : MIT Press
Page : 289 pages
File Size : 46,6 Mb
Release : 2023-05-09
Category : Computers
ISBN : 9780262547833

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Machine Learning for Data Streams by Albert Bifet,Ricard Gavalda,Geoffrey Holmes,Bernhard Pfahringer Pdf

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.

Machine Intelligence and Big Data Analytics for Cybersecurity Applications

Yassine Maleh,Mohammad Shojafar,Mamoun Alazab,Youssef Baddi
Machine Intelligence and Big Data Analytics for Cybersecurity Applications Book PDF

Author : Yassine Maleh,Mohammad Shojafar,Mamoun Alazab,Youssef Baddi
Publisher : Springer Nature
Page : 539 pages
File Size : 51,9 Mb
Release : 2020-12-14
Category : Computers
ISBN : 9783030570248

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Machine Intelligence and Big Data Analytics for Cybersecurity Applications by Yassine Maleh,Mohammad Shojafar,Mamoun Alazab,Youssef Baddi Pdf

This book presents the latest advances in machine intelligence and big data analytics to improve early warning of cyber-attacks, for cybersecurity intrusion detection and monitoring, and malware analysis. Cyber-attacks have posed real and wide-ranging threats for the information society. Detecting cyber-attacks becomes a challenge, not only because of the sophistication of attacks but also because of the large scale and complex nature of today’s IT infrastructures. It discusses novel trends and achievements in machine intelligence and their role in the development of secure systems and identifies open and future research issues related to the application of machine intelligence in the cybersecurity field. Bridging an important gap between machine intelligence, big data, and cybersecurity communities, it aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this area or those interested in grasping its diverse facets and exploring the latest advances on machine intelligence and big data analytics for cybersecurity applications.

Mining of Massive Datasets

Jure Leskovec,Jurij Leskovec,Anand Rajaraman,Jeffrey David Ullman
Mining of Massive Datasets Book PDF

Author : Jure Leskovec,Jurij Leskovec,Anand Rajaraman,Jeffrey David Ullman
Publisher : Cambridge University Press
Page : 480 pages
File Size : 48,5 Mb
Release : 2014-11-13
Category : Computers
ISBN : 9781107077232

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Mining of Massive Datasets by Jure Leskovec,Jurij Leskovec,Anand Rajaraman,Jeffrey David Ullman Pdf

Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.

Knowledge Discovery from Data Streams

Joao Gama
Knowledge Discovery from Data Streams Book PDF

Author : Joao Gama
Publisher : CRC Press
Page : 256 pages
File Size : 41,7 Mb
Release : 2010-05-25
Category : Business & Economics
ISBN : 9781439826126

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Knowledge Discovery from Data Streams by Joao Gama Pdf

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

Managing and Mining Uncertain Data

Charu C. Aggarwal
Managing and Mining Uncertain Data Book PDF

Author : Charu C. Aggarwal
Publisher : Springer
Page : 494 pages
File Size : 46,6 Mb
Release : 2010-07-08
Category : Computers
ISBN : 0387096906

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Managing and Mining Uncertain Data by Charu C. Aggarwal Pdf

Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.