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Stream Data Mining Algorithms And Their Probabilistic Properties
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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 Streams written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2007-04-03 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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 Mining In Time Series And Streaming Databases by : Mark Last
Download or read book Data Mining In Time Series And Streaming Databases written by Mark Last and published by World Scientific. This book was released on 2018-01-12 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Artificial Intelligence and Soft Computing by : Leszek Rutkowski
Download or read book Artificial Intelligence and Soft Computing written by Leszek Rutkowski and published by Springer. This book was released on 2019-05-27 with total page 712 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Mining of Massive Datasets by : Jure Leskovec
Download or read book Mining of Massive Datasets written by Jure Leskovec and published by Cambridge University Press. This book was released on 2014-11-13 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
Book Synopsis Adaptivity in Data Stream Mining by : Conny Franke
Download or read book Adaptivity in Data Stream Mining written by Conny Franke and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years data streams became a ubiquitous source of information, and thus stream mining emerged as a new field in database research. Due to the inherently dynamic nature of data streams, stream mining algorithms benefit from being adaptive to changes in the properties of a data stream. In addition, when stream mining is done in a dynamic environment like a data stream management system or a sensor network, stream mining algorithms also profit from being adaptive to the changing conditions in this environment. This work investigates two kinds of adaptivity in data stream mining. First, a model for quality-driven resource adaptive stream mining is developed. The model is applied to stream mining algorithms so they efficiently utilize available resources to achieve mining results of the highest quality possible. Every stream mining algorithm is unique in its parameters, quality measures, and resource consumption patterns. We generalize these characteristics and develop a model that captures the interactions and correlations between variables involved in the stream mining process. We then express resource adaptive stream mining as a multiobjective optimization problem and use its solution to tune the input parameters of stream mining algorithms, which results in high quality mining and optimal resource utilization. The second topic investigated in this work is feature adaptive stream mining, which is concerned with adjusting the focus of the mining process to interesting features detected in the data stream. This research is motivated by the need to efficiently detect environmental phenomena from sensor data streams. We propose methods to detect and predict heterogeneous outlier regions, which represent areas of environmental phenomena of different intensities. With the help of predictions about the location and size of outlier regions, the sampling rate of individual sensors is adapted such that sensors in the vicinity of environmental phenomena obtain new measurements more frequently than other sensors in the network to allow for a precise and timely region tracking. The research in this work enhances the state-of-the-art in data stream mining as it makes stream mining algorithms more flexible to adapt to changes in the data stream and the mining environment.
Book Synopsis Neural Information Processing by : Tom Gedeon
Download or read book Neural Information Processing written by Tom Gedeon and published by Springer Nature. This book was released on 2019-12-05 with total page 802 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Cloud Computing, Big Data & Emerging Topics by : Enzo Rucci
Download or read book Cloud Computing, Big Data & Emerging Topics written by Enzo Rucci and published by Springer Nature. This book was released on 2022-08-04 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Dynamic Data Mining written by and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Streams written by S. Muthukrishnan and published by Now Publishers Inc. This book was released on 2005 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Researchers in Theoretical Computer Science, Databases, IP Networking and Computer Systems are working on the data stream challenges.
Book Synopsis Connected e-Health by : Sushruta Mishra
Download or read book Connected e-Health written by Sushruta Mishra and published by Springer Nature. This book was released on 2022-05-05 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Intelligence and Big Data Analytics for Cybersecurity Applications by : Yassine Maleh
Download or read book Machine Intelligence and Big Data Analytics for Cybersecurity Applications written by Yassine Maleh and published by Springer Nature. This book was released on 2020-12-14 with total page 539 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Principles of Data Mining by : Max Bramer
Download or read book Principles of Data Mining written by Max Bramer and published by Springer Nature. This book was released on 2020-05-20 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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, it 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. Principles of Data Mining includes 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. The expanded fourth edition gives a detailed description of a feed-forward neural network with backpropagation and shows how it can be used for classification.
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 Techniques in Data Stream Mining by : Suk-Man Ivy Tong
Download or read book Techniques in Data Stream Mining written by Suk-Man Ivy Tong and published by . This book was released on 2017-01-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Techniques in Data Stream Mining" by Suk-man, Ivy, Tong, 湯淑敏, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled Techniques in Data Stream Mining submitted by Tong Suk-man Ivy for the degree of Master of Philosophy at The University of Hong Kong in November 2005 Many organizations have been confronted by a data explosion in the last decade, and face the problem of managing very large databases that grow at a rate of sev- eral million records per day. To address this problem, database and data mining communities have recently focused on stream processing, where data arrives in the form of continuous data streams. Efficient stream mining is challenging yet critical. However, it is not feasible to perform traditional data mining algorithms on streaming data is infeasible. They have a number of limitations: 1) Most of the classical mining algorithms take multiple passes over the entire database, but the speed of arrival and the volume of the data streams makes it impossible to store them. 2) Timely response is important in stream applications. Disk-based algorithms are inappropriate. 3) Since only a small representation of the whole dataset is kept, approximate algorithms with high accuracy are needed. This study explores some techniques in data stream mining. In particular, it focuses on data from multiple sensor streams, where each stream represents a sequence of states of a monitored attribute reported by a sensor against time. (In finance, a stream may be a stock, for example.) The first technique proposed in this study is a modification of Vitter's reser-voir sampling algorithm, which can generate a fixed-size uniform sample set from an input stream without a priori knowledge of the size of the stream. Applying reservoir sampling on each stream individually would give a sample of time- uncorrelated points from different sensor streams. That is, the sensor states sampled for different streams do not co-exist within any time span. The sample obtained is therefore useless for answering queries related to associations of the streams. Instead of sampling streams individually, a sample of snapshots taken of the streams at different time instants is generated. This ensures that if the state of a stream in a certain time-span is sampled, the states of other streams in the time-span must be in the sample. The second technique is used in mining frequent patterns from a large sensor network. Data representation of sensor streams affects the efficiency of online mining. Based on the estimation mechanism of the Lossy Counting (LC) algo- rithm, a window based algorithm (ILB) which makes use of interval-list represen- tation is proposed. Experiments on synthetic datasets were conducted to show the efficiency of our ILB algorithms. Experimental results showed that if the sensor network is massive, the ILB algorithms outperform LC by a significant margin. DOI: 10.5353/th_b3473737 Subjects: Database management Data mining
Book Synopsis Distributed and Stream Data Mining Algorithms for Frequent Pattern Discovery by : Claudio Silvestri
Download or read book Distributed and Stream Data Mining Algorithms for Frequent Pattern Discovery written by Claudio Silvestri and published by . This book was released on 2006 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
