Temporal Data Mining
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Temporal Data Mining
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Author : Theophano Mitsa
language : en
Publisher: CRC Press
Release Date : 2010-03-10
Temporal Data Mining written by Theophano Mitsa and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-03-10 with Business & Economics categories.
From basic data mining concepts to state-of-the-art advances, this book covers the theory of the subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, web usage mining, and spatiotemporal data mining. Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references.
Temporal And Spatio Temporal Data Mining
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Author : Hsu, Wynne
language : en
Publisher: IGI Global
Release Date : 2007-07-31
Temporal And Spatio Temporal Data Mining written by Hsu, Wynne and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-31 with Computers categories.
"This book presents probable solutions when discovering the spatial sequence patterns by incorporating the information into the sequence of patterns, and introduces new classes of spatial sequence patterns, called flow and generalized spatio-temporal patterns, addressing different scenarios in spatio-temporal data by modeling them as graphs, providing a comprehensive synopsis on two successful partition-based algorithms designed by the authors"--Provided by publisher.
Time Granularities In Databases Data Mining And Temporal Reasoning
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Author : Claudio Bettini
language : en
Publisher: Springer Science & Business Media
Release Date : 2000-07-13
Time Granularities In Databases Data Mining And Temporal Reasoning written by Claudio Bettini 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 2000-07-13 with Computers categories.
Calendar and time units and specialized units, such as business days and academic years, play a major role in a wide range of information system applications. System support for reasoning about these units, called granularities, is important for the efficient design, use, and implementation of such applications. This book deals with several aspects of temporal information and provides a unifying model for granularities. Practitioners can learn about critical aspects that must be taken into account when designing and implementing databases supporting temporal information.
Temporal Data Mining Via Unsupervised Ensemble Learning
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Author : Yun Yang
language : en
Publisher: Elsevier
Release Date : 2016-11-21
Temporal Data Mining Via Unsupervised Ensemble Learning written by Yun Yang and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-21 with Computers categories.
Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice. Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem. Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics.
Temporal Spatial And Spatio Temporal Data Mining
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Author : John F. Roddick
language : en
Publisher:
Release Date : 2014-01-15
Temporal Spatial And Spatio Temporal Data Mining written by John F. Roddick and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.
Temporal Spatial And Spatio Temporal Data Mining
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Author : John F. Roddick
language : en
Publisher: Springer Science & Business Media
Release Date : 2001-02-28
Temporal Spatial And Spatio Temporal Data Mining written by John F. Roddick 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 2001-02-28 with Computers categories.
This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining, TSDM 2000, held in Lyon, France in September 2000 during the PKDD 2000 conference. The ten revised full papers presented are complemented by an introductory workshop report and an updated bibliography for the emerging new field; this bibliography is organized in nine topical chapters and lists more than 150 entries. All in all, the volume reflects the state of the art in the area and sets the scene for future R & D activities.
Outlier Detection For Temporal Data
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Author : Manish Gupta
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Outlier Detection For Temporal Data written by Manish Gupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies
Spatial Data Mining
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Author : Deren Li
language : en
Publisher: Springer
Release Date : 2016-03-23
Spatial Data Mining written by Deren Li and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-23 with Computers categories.
· This book is an updated version of a well-received book previously published in Chinese by Science Press of China (the first edition in 2006 and the second in 2013). It offers a systematic and practical overview of spatial data mining, which combines computer science and geo-spatial information science, allowing each field to profit from the knowledge and techniques of the other. To address the spatiotemporal specialties of spatial data, the authors introduce the key concepts and algorithms of the data field, cloud model, mining view, and Deren Li methods. The data field method captures the interactions between spatial objects by diffusing the data contribution from a universe of samples to a universe of population, thereby bridging the gap between the data model and the recognition model. The cloud model is a qualitative method that utilizes quantitative numerical characters to bridge the gap between pure data and linguistic concepts. The mining view method discriminates the different requirements by using scale, hierarchy, and granularity in order to uncover the anisotropy of spatial data mining. The Deren Li method performs data preprocessing to prepare it for further knowledge discovery by selecting a weight for iteration in order to clean the observed spatial data as much as possible. In addition to the essential algorithms and techniques, the book provides application examples of spatial data mining in geographic information science and remote sensing. The practical projects include spatiotemporal video data mining for protecting public security, serial image mining on nighttime lights for assessing the severity of the Syrian Crisis, and the applications in the government project ‘the Belt and Road Initiatives’.
Advanced Data Mining And Applications
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Author : Xue Li
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-07-26
Advanced Data Mining And Applications written by Xue Li 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 2006-07-26 with Computers categories.
Here are the proceedings of the 2nd International Conference on Advanced Data Mining and Applications, ADMA 2006, held in Xi'an, China, August 2006. The book presents 41 revised full papers and 74 revised short papers together with 4 invited papers. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, and more.