Adaptivity In Data Stream Mining
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Adaptivity In Data Stream Mining
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Author : Conny Franke
language : en
Publisher:
Release Date : 2009
Adaptivity In Data Stream Mining written by Conny Franke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
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.
Adaptive Stream Mining
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Author : Albert Bifet
language : en
Publisher: IOS Press
Release Date : 2010
Adaptive Stream Mining written by Albert Bifet and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.
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.
Adaptive Learning And Mining For Data Streams And Frequent Patterns
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Author : Albert Bifet Figuerol
language : en
Publisher:
Release Date : 2009
Adaptive Learning And Mining For Data Streams And Frequent Patterns written by Albert Bifet Figuerol and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Advances In Knowledge Discovery And Data Mining
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Author : Honghua Dai
language : en
Publisher: Springer Science & Business Media
Release Date : 2004-05-11
Advances In Knowledge Discovery And Data Mining written by Honghua Dai and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-05-11 with Business & Economics categories.
This book constitutes the refereed proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data mining, PAKDD 2004, held in Sydney, Australia in May 2004. The 50 revised full papers and 31 revised short papers presented were carefully reviewed and selected from a total of 238 submissions. The papers are organized in topical sections on classification; clustering; association rules; novel algorithms; event mining, anomaly detection, and intrusion detection; ensemble learning; Bayesian network and graph mining; text mining; multimedia mining; text mining and Web mining; statistical methods, sequential data mining, and time series mining; and biomedical data mining.
Machine Learning For Data Streams
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Author : Albert Bifet
language : en
Publisher: MIT Press
Release Date : 2018-03-16
Machine Learning For Data Streams written by Albert Bifet and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-16 with Computers categories.
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.
Adaptive Learning And Mining For Data Streams And Frequent Patterns
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Author : Albert Bifet Figuerol
language : en
Publisher:
Release Date : 2009
Adaptive Learning And Mining For Data Streams And Frequent Patterns written by Albert Bifet Figuerol and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Machine Learning For Data Streams
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Author : Albert Bifet
language : en
Publisher: MIT Press
Release Date : 2023-05-09
Machine Learning For Data Streams written by Albert Bifet and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-09 with Computers categories.
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.
Adaptive Mining Of Data Streams In Resource Constrained Environments
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Author : Mohamed M. Medhat M. Gaber
language : en
Publisher:
Release Date : 2006
Adaptive Mining Of Data Streams In Resource Constrained Environments written by Mohamed M. Medhat M. Gaber and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Algorithms categories.
Data Streams Mining
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Author : Kapil Wankhade
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2010-12
Data Streams Mining written by Kapil Wankhade and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-12 with categories.
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.
Knowledge Discovery From Sensor Data
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Author : Mohamed Medhat Gaber
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-04-14
Knowledge Discovery From Sensor Data written by Mohamed Medhat Gaber and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-04-14 with Computers categories.
This book contains thoroughly refereed extended papers from the Second International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008, held in Las Vegas, NV, USA, in August 2008. The 12 revised papers presented together with an invited paper were carefully reviewed and selected from numerous submissions. The papers feature important aspects of knowledge discovery from sensor data, e.g., data mining for diagnostic debugging; incremental histogram distribution for change detection; situation-aware adaptive visualization; WiFi mining; mobile sensor data mining; incremental anomaly detection; and spatiotemporal neighborhood discovery for sensor data.