[PDF] Adaptive Stream Mining eBook

Author : Albert Bifet
Publisher : IOS Press
Page : 224 pages
File Size : 45,67 MB
Release : 2010
Category : Computers
ISBN : 1607500906

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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 : Conny Franke
Publisher :
Page : pages
File Size : 16,55 MB
Release : 2009
Category :
ISBN : 9781109661774

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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.

Author : Albert Bifet
Publisher : MIT Press
Page : 255 pages
File Size : 41,76 MB
Release : 2018-03-16
Category : Computers
ISBN : 0262346052

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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 : Honghua Dai
Publisher : Springer Science & Business Media
Page : 731 pages
File Size : 35,35 MB
Release : 2004-05-11
Category : Business & Economics
ISBN : 354022064X

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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.

Author : Zhi-Hua Zhou
Publisher : Springer
Page : 426 pages
File Size : 20,45 MB
Release : 2009-11-03
Category : Computers
ISBN : 364205224X

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The First Asian Conference on Machine Learning (ACML 2009) was held at Nanjing, China during November 2–4, 2009.This was the ?rst edition of a series of annual conferences which aim to provide a leading international forum for researchers in machine learning and related ?elds to share their new ideas and research ?ndings. This year we received 113 submissions from 18 countries and regions in Asia, Australasia, Europe and North America. The submissions went through a r- orous double-blind reviewing process. Most submissions received four reviews, a few submissions received ?ve reviews, while only several submissions received three reviews. Each submission was handled by an Area Chair who coordinated discussions among reviewers and made recommendation on the submission. The Program Committee Chairs examined the reviews and meta-reviews to further guarantee the reliability and integrity of the reviewing process. Twenty-nine - pers were selected after this process. To ensure that important revisions required by reviewers were incorporated into the ?nal accepted papers, and to allow submissions which would have - tential after a careful revision, this year we launched a “revision double-check” process. In short, the above-mentioned 29 papers were conditionally accepted, and the authors were requested to incorporate the “important-and-must”re- sionssummarizedbyareachairsbasedonreviewers’comments.Therevised?nal version and the revision list of each conditionally accepted paper was examined by the Area Chair and Program Committee Chairs. Papers that failed to pass the examination were ?nally rejected.

Author :
Publisher :
Page : 32 pages
File Size : 46,80 MB
Release : 2002
Category :
ISBN :

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Sensor devices and embedded processors are becoming ubiquitous, especially in measurement and monitoring applications. Automatic discovery of patterns and trends in the large volumes of such data is of paramount importance. The combination of relatively limited resources (CPU, memory and/or communication bandwidth and power) poses some interesting challenges. We need both powerful and concise languages to represent the important features of the data, which can (a) adapt and handle arbitrary periodic components, including bursts, and (b) require little memory and a single pass over the data. This allows sensors to automatically (a) discover interesting patterns and trends in the data, and (b) perform outlier detection to alert users. We need a way so that a sensor can discover something like the hourly phone call volume so far follows a daily and a weekly periodicity, with bursts roughly every year, which a human might recognize as, e.g., the Mother's Day surge. When possible and if desired, the user can then issue explicit queries to further investigate the reported patterns. In this work we propose AWSOM (Arbitrary Window Stream mOdeling Method), which allows sensors operating in remote or hostile environments to discover patterns efficiently and effectively, with practically no user intervention. Our algorithms require limited resources and can thus be incorporated in individual sensors, possibly alongside a distributed query processing engine [CCC+02, BGS01, MSHR02]. Updates are performed in constant time, using sub-linear (in fact, logarithmic) space. Existing, state of the art forecasting methods (AR, SARIMA, GARCH, etc.) fall short on one or more of these requirements. To the best of our knowledge, AWSOM is the first method that has all the above characteristics.

Author : Joao Gama
Publisher : CRC Press
Page : 256 pages
File Size : 29,1 MB
Release : 2010-05-25
Category : Business & Economics
ISBN : 1439826129

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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 : João Gama
Publisher : Springer Science & Business Media
Page : 486 pages
File Size : 41,42 MB
Release : 2007-10-11
Category : Computers
ISBN : 3540736786

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Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. Applications in security, the natural sciences, and education are presented. The huge bibliography offers an excellent starting point for further reading and future research.

Author : Christos Faloutsos
Publisher : Springer Science & Business Media
Page : 172 pages
File Size : 32,59 MB
Release : 1996-09-30
Category : Computers
ISBN : 9780792397779

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Searching Multimedia Databases by Content bridges the gap between the database and signal processing communities by providing the necessary background information for the reader and presenting it along with the intuition and mechanics of the best existing tools in each area. The first half of Searching Multimedia Databases by Content reviews the most successful database access methods, in increasing complexity, reaching up to spatial access methods and text retrieval. In all cases, the emphasis is on practical approaches that have been incorporated in commercial systems, or that seem very promising. The second half of the book uses the above access methods to achieve fast searching in a database of signals. A general methodology is presented, which suggests extracting a few good features from each multimedia object, thus mapping objects into points in a metric space. Finally, the book concludes by presenting some recent successful applications of the methodology on time series and color images. Searching Multimedia Databases by Content is targeted towards researchers and developers of multimedia systems. The book can also serve as a textbook for a graduate course on multimedia searching, covering both access methods as well as the basics of signal processing.