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Nomenclature Of Datastreams Mining Strategies Concept Drift And Research Objectives
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Book Synopsis Nomenclature of Datastreams, Mining Strategies, Concept Drift and Research Objectives by : Dr. Annaluri Sreenivasa Rao
Download or read book Nomenclature of Datastreams, Mining Strategies, Concept Drift and Research Objectives written by Dr. Annaluri Sreenivasa Rao and published by Shineeks Publishers. This book was released on 2022-03-16 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: Streaming data is one of the primary sources of what is known as big data. While data streams and big data have gotten a lot of attention in the recent decade, many research methodologies are often intended for well-behaved controlled problem settings, overlooking major obstacles given by real-world applications. The eight open difficulties for data stream mining are discussed in this book. Our goal is to discover gaps between present research and useful applications, to highlight unresolved issues, and to create new data stream mining research lines that are relevant to applications.
Book Synopsis Stream Data Mining: Algorithms and Their Probabilistic Properties by : Leszek Rutkowski
Download or read book Stream Data Mining: Algorithms and Their Probabilistic Properties written by Leszek Rutkowski and published by Springer. This book was released on 2019-03-16 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data Mining written by Florin Gorunescu and published by Springer Science & Business Media. This book was released on 2011-03-10 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: The knowledge discovery process is as old as Homo sapiens. Until some time ago this process was solely based on the ‘natural personal' computer provided by Mother Nature. Fortunately, in recent decades the problem has begun to be solved based on the development of the Data mining technology, aided by the huge computational power of the 'artificial' computers. Digging intelligently in different large databases, data mining aims to extract implicit, previously unknown and potentially useful information from data, since “knowledge is power”. The goal of this book is to provide, in a friendly way, both theoretical concepts and, especially, practical techniques of this exciting field, ready to be applied in real-world situations. Accordingly, it is meant for all those who wish to learn how to explore and analysis of large quantities of data in order to discover the hidden nugget of information.
Book Synopsis Adaptive Stream Mining by : Albert Bifet
Download or read book Adaptive Stream Mining written by Albert Bifet and published by IOS Press. This book was released on 2010 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Streams Mining by : Kapil Wankhade
Download or read book Data Streams Mining written by Kapil Wankhade and published by LAP Lambert Academic Publishing. This book was released on 2010-12 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge discovery and data mining from time changing data streams and concept drift handling on data streams have become important topics in the machine learning recently. Machine learning offers promise of a solution, but the field mainly focuses on achieving high accuracy when data supply is limited. The challenges that are faced by information processing and classification in particular, are related to the need to cope with huge volume of data, to process data streams online and in real time and to handle concept drift. When tackling with data stream, incremental classification algorithms are required. An ensemble of classifiers has several advantages over single classifier methods. So we have designed and implemented a new ensemble classifier which is adaptive and efficient for data streams classification. Adaptive sliding window and adaptive size hoeffding tree techniques are used in this algorithm. This technique should helpful to online processing of data streams and should be especially useful to network monitoring systems and financial industries or anyone else who may be handling data streams.
Book Synopsis Knowledge Discovery from Data Streams by : Joao Gama
Download or read book Knowledge Discovery from Data Streams written by Joao Gama and published by CRC Press. This book was released on 2010-05-25 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Optimum-Path Forest by : Alexandre Xavier Falcao
Download or read book Optimum-Path Forest written by Alexandre Xavier Falcao and published by Elsevier. This book was released on 2022-01-24 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimum-Path Forest: Theory, Algorithms, and Applications was first published in 2008 in its supervised and unsupervised versions with applications in medicine and image classification. Since then, it has expanded to a variety of other applications such as remote sensing, electrical and petroleum engineering, and biology. In recent years, multi-label and semi-supervised versions were also developed to handle video classification problems. The book presents the principles, algorithms and applications of Optimum-Path Forest, giving the theory and state-of-the-art as well as insights into future directions. Presents the first book on Optimum-path Forest Shows how it can be used with Deep Learning Gives a wide range of applications Includes the methods, underlying theory and applications of Optimum-Path Forest (OPF)
Book Synopsis Intelligent Computing for Sustainable Development by : S. Satheeskumaran
Download or read book Intelligent Computing for Sustainable Development written by S. Satheeskumaran and published by Springer Nature. This book was released on with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Collaborative Filtering Using Data Mining and Analysis by : Bhatnagar, Vishal
Download or read book Collaborative Filtering Using Data Mining and Analysis written by Bhatnagar, Vishal and published by IGI Global. This book was released on 2016-07-13 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Internet usage has become a normal and essential aspect of everyday life. Due to the immense amount of information available on the web, it has become obligatory to find ways to sift through and categorize the overload of data while removing redundant material. Collaborative Filtering Using Data Mining and Analysis evaluates the latest patterns and trending topics in the utilization of data mining tools and filtering practices. Featuring emergent research and optimization techniques in the areas of opinion mining, text mining, and sentiment analysis, as well as their various applications, this book is an essential reference source for researchers and engineers interested in collaborative filtering.
Book Synopsis Machine Learning Techniques for Improved Business Analytics by : G., Dileep Kumar
Download or read book Machine Learning Techniques for Improved Business Analytics written by G., Dileep Kumar and published by IGI Global. This book was released on 2018-07-06 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analytical tools and algorithms are essential in business data and information systems. Efficient economic and financial forecasting in machine learning techniques increases gains while reducing risks. Providing research on predictive models with high accuracy, stability, and ease of interpretation is important in improving data preparation, analysis, and implementation processes in business organizations. Machine Learning Techniques for Improved Business Analytics is a collection of innovative research on the methods and applications of artificial intelligence in strategic business decisions and management. Featuring coverage on a broad range of topics such as data mining, portfolio optimization, and social network analysis, this book is ideally designed for business managers and practitioners, upper-level business students, and researchers seeking current research on large-scale information control and evaluation technologies that exceed the functionality of conventional data processing techniques.
Download or read book Metalearning written by Pavel Brazdil and published by Springer Science & Business Media. This book was released on 2008-11-26 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.
Book Synopsis Big Data Analytics with Applications in Insider Threat Detection by : Bhavani Thuraisingham
Download or read book Big Data Analytics with Applications in Insider Threat Detection written by Bhavani Thuraisingham and published by CRC Press. This book was released on 2017-11-22 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection.
Book Synopsis Proceedings of the 12th International Conference on Soft Computing for Problem Solving by : Millie Pant
Download or read book Proceedings of the 12th International Conference on Soft Computing for Problem Solving written by Millie Pant and published by Springer Nature. This book was released on with total page 922 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Management Association, Information Resources Publisher :IGI Global ISBN 13 :1466624566 Total Pages :2335 pages Book Rating :4.4/5 (666 download)
Book Synopsis Data Mining: Concepts, Methodologies, Tools, and Applications by : Management Association, Information Resources
Download or read book Data Mining: Concepts, Methodologies, Tools, and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2012-11-30 with total page 2335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining continues to be an emerging interdisciplinary field that offers the ability to extract information from an existing data set and translate that knowledge for end-users into an understandable way. Data Mining: Concepts, Methodologies, Tools, and Applications is a comprehensive collection of research on the latest advancements and developments of data mining and how it fits into the current technological world.
Author :Moamar Sayed-Mouchaweh Publisher :Springer Science & Business Media ISBN 13 :1441980202 Total Pages :439 pages Book Rating :4.4/5 (419 download)
Book Synopsis Learning in Non-Stationary Environments by : Moamar Sayed-Mouchaweh
Download or read book Learning in Non-Stationary Environments written by Moamar Sayed-Mouchaweh and published by Springer Science & Business Media. This book was released on 2012-04-13 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations. This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.
Book Synopsis New Frontiers in Mining Complex Patterns by : Michelangelo Ceci
Download or read book New Frontiers in Mining Complex Patterns written by Michelangelo Ceci and published by Springer. This book was released on 2016-05-17 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2015, held in conjunction with ECML-PKDD 2015 in Porto, Portugal, in September 2015. The 15 revised full papers presented together with one invited talk were carefully reviewed and selected from 19 submissions. They illustrate advanced data mining techniques which preserve the informative richness of complex data and allow for efficient and effective identification of complex information units present in such data. The papers are organized in the following sections: data stream mining, classification, mining complex data, and sequences.
Book Synopsis Process Mining Handbook by : Wil M. P. van der Aalst
Download or read book Process Mining Handbook written by Wil M. P. van der Aalst and published by Springer Nature. This book was released on 2022-06-27 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an open access book. This book comprises all the single courses given as part of the First Summer School on Process Mining, PMSS 2022, which was held in Aachen, Germany, during July 4-8, 2022. This volume contains 17 chapters organized into the following topical sections: Introduction; process discovery; conformance checking; data preprocessing; process enhancement and monitoring; assorted process mining topics; industrial perspective and applications; and closing.
