Adaptive Learning and Mining for Data Streams and Frequent Patterns
Author : Albert Bifet Figuerol
Publisher :
Page : 218 pages
File Size : 18,28 MB
Release : 2009
Category :
ISBN :
Author : Albert Bifet Figuerol
Publisher :
Page : 218 pages
File Size : 18,28 MB
Release : 2009
Category :
ISBN :
Author : Albert Bifet Figuerol
Publisher :
Page : 218 pages
File Size : 17,88 MB
Release : 2009
Category :
ISBN :
Author : Albert Bifet
Publisher : IOS Press
Page : 224 pages
File Size : 39,40 MB
Release : 2010
Category : Computers
ISBN : 1607500906
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.
Author : Albert Bifet
Publisher : MIT Press
Page : 255 pages
File Size : 38,58 MB
Release : 2018-03-16
Category : Computers
ISBN : 0262346052
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.
Author : Albert Bifet
Publisher : MIT Press
Page : 289 pages
File Size : 30,12 MB
Release : 2023-05-09
Category : Computers
ISBN : 026254783X
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.
Author : Charu C. Aggarwal
Publisher : Springer
Page : 480 pages
File Size : 35,19 MB
Release : 2014-08-29
Category : Computers
ISBN : 3319078216
This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference.
Author : Tiwari, Vivek
Publisher : IGI Global
Page : 425 pages
File Size : 30,33 MB
Release : 2018-04-20
Category : Computers
ISBN : 1522538712
Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. The Handbook of Research on Pattern Engineering System Development for Big Data Analytics is a critical scholarly resource that examines the incorporation of pattern management in business technologies as well as decision making and prediction process through the use of data management and analysis. Featuring coverage on a broad range of topics such as business intelligence, feature extraction, and data collection, this publication is geared towards professionals, academicians, practitioners, and researchers seeking current research on the development of pattern management systems for business applications.
Author : Marek Kurzynski
Publisher : Springer
Page : 522 pages
File Size : 36,83 MB
Release : 2017-05-05
Category : Technology & Engineering
ISBN : 3319591622
This book offers a comprehensive study of computer recognition systems – one of the most promising directions in artificial intelligence. It presents a collection of 52 carefully selected articles contributed by experts in the field of pattern recognition, discussing both methodological aspects and applications of current research. It includes the following sections: · Features, learning, and classifiers · Biometrics · Data stream classification and big data analytics · Image processing and computer vision · Medical applications · Applications It is a valuable reference tool for scientists dealing with the problems of designing computer pattern recognition systems, including researchers and students of computer science, artificial intelligence and robotics.
Author : Joao Gama
Publisher : CRC Press
Page : 256 pages
File Size : 39,18 MB
Release : 2010-05-25
Category : Business & Economics
ISBN : 1439826129
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
Author : Hamido Fujita
Publisher : Springer
Page : 1019 pages
File Size : 45,84 MB
Release : 2016-07-13
Category : Computers
ISBN : 3319420070
This book constitutes the refereed conference proceedings of the 29th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, held in Morioka, Japan, in August 2-4, 2016. The 80 revised full papers presented were carefully reviewed and selected from 168 submissions. They are organized in topical sections: data science; knowledge base systems; natural language processing and sentiment analysis; semantic Web and social networks; computer vision; medical diagnosis system and bio-informatics; applied neural networks; innovations in intelligent systems and applications; decision support systems; adaptive control; soft computing and multi-agent systems; evolutionary algorithms and heuristic search; system integration for real-life applications.