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Author: Cristina Stolojescu
Publisher:
ISBN: 9786065544185
Category :
Languages : en
Pages : 109
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Book Description
Author: Cristina Stolojescu
Publisher:
ISBN: 9786065544185
Category :
Languages : en
Pages : 109
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Book Description
Author: Christina-Laura Stolojescu
Publisher:
ISBN:
Category :
Languages : fr
Pages : 111
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Book Description
Cette thèse est basée sur la recherche des méthodes d'analyse des séries temporelles. L'approche choisie dans cette thèse est fondée sur l'analyse d'une base de données conçue après la surveillance du trafique dans un réseau WiMAX. Prenant en compte le volume d'information important contenu dans cette base de données, on a choisi une approche de type fouille de données. En supposant que le trafique associé avec une BS mal positionnée est plus lourde (moins fluent) que le trafique associé avec une station de base bien positionnée, on a élaboré deux approches pour l'évaluation de la fluence du trafique. La première approche est basée sur la supposition que le risque de saturation d'une BS avec trafique lourde est réduit. En conséquence, il est nécessaire d'estimer le risque de saturation de chaque station de base. Donc, le premier objectif de cette thèse est de proposer une approche pour la prédiction des séries temporelles. Cette approche est basée sur une analyse multi résolution (MRA) du signal associée à une décomposition orthogonale réalisées à l'aide de la transformée en ondelettes stationnaire (SWT) suivie par une modélisation statistique à l'aide des modèles ARIMA. La deuxième approche pour l'évaluation de la fluence du trafique est basée sur l'analyse de la LRD des séries temporelles qui composent la base de données. L'estimation du dégrée de LRD se fait par l'estimation du paramètre de Hurst (H) de la série temporelle analysée. Les résultats indiquent les BS qui ont un mauvais positionnement. Ces dernières BS doivent être repositionnées à l'occasion de la suivante session de maintenance du réseau.
Author: Donald B. Percival
Publisher: Cambridge University Press
ISBN: 1107717396
Category : Mathematics
Languages : en
Pages : 628
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Book Description
This introduction to wavelet analysis 'from the ground level and up', and to wavelet-based statistical analysis of time series focuses on practical discrete time techniques, with detailed descriptions of the theory and algorithms needed to understand and implement the discrete wavelet transforms. Numerous examples illustrate the techniques on actual time series. The many embedded exercises - with complete solutions provided in the Appendix - allow readers to use the book for self-guided study. Additional exercises can be used in a classroom setting. A Web site offers access to the time series and wavelets used in the book, as well as information on accessing software in S-Plus and other languages. Students and researchers wishing to use wavelet methods to analyze time series will find this book essential.
Author: Thomas Meinl
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: Charu C. Aggarwal
Publisher: Springer
ISBN: 3319141422
Category : Computers
Languages : en
Pages : 746
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Book Description
This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago
Author: Yun Yang
Publisher: Elsevier
ISBN: 0128118415
Category : Computers
Languages : en
Pages : 174
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Book Description
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. - Includes fundamental concepts and knowledge, covering all key tasks and techniques of temporal data mining, i.e., temporal data representations, similarity measure, and mining tasks - Concentrates on temporal data clustering tasks from different perspectives, including major algorithms from clustering algorithms and ensemble learning approaches - Presents a rich blend of theory and practice, addressing seminal research ideas and looking at the technology from a practical point-of-view
Author: Oded Maimon
Publisher: Springer Science & Business Media
ISBN: 038725465X
Category : Computers
Languages : en
Pages : 1378
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Book Description
Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
Author: Xue Li
Publisher: Springer Science & Business Media
ISBN: 3540370250
Category : Computers
Languages : en
Pages : 1130
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Book Description
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.
Author: Longbing Cao
Publisher: Springer
ISBN: 3642283209
Category : Computers
Languages : en
Pages : 526
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Book Description
This book constitutes the thoroughly refereed post-conference proceedings of five international workshops held in conjunction with PAKDD 2011 in Shenzhen, China, in May 2011: the International Workshop on Behavior Informatics (BI 2011), the Workshop on Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models (QIMIE 2011), the Workshop on Biologically Inspired Techniques for Data Mining (BDM 2011), the Workshop on Advances and Issues in Traditional Chinese Medicine Clinical Data Mining (AI-TCM 2011), and the Second Workshop on Data Mining for Healthcare Management (DMGHM 2011). The book also includes papers from the First PAKDD Doctoral Symposium on Data Mining (DSDM 2011). The 42 papers were carefully reviewed and selected from numerous submissions. The papers cover a wide range of topics discussing emerging techniques in the field of knowledge discovery in databases and their application domains extending to previously unexplored areas such as data mining based on optimization techniques from biological behavior of animals and applications in Traditional Chinese Medicine clinical research and health care management.
Author: Elizabeth Ann Maharaj
Publisher: CRC Press
ISBN: 0429603304
Category : Mathematics
Languages : en
Pages : 213
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Book Description
The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including unsupervised and supervised approaches Includes a range of real examples from medicine, finance, environmental science, and more R and MATLAB code, and relevant data sets are available on a supplementary website
