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Time Series Clustering and Classification
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
COMPSTAT
This book contains the keynote, invited and full contributed papers presented at COMPSTAT 2000, held in Utrecht. The papers range over all aspects of the link between statistical theory and applied statistics, with special attention for developments in the area of official statistics. The papers have been thoroughly refereed.
Compstat
This COMPSTAT 2002 book contains the Keynote, Invited, and Full Contributed papers presented in Berlin, August 2002. A companion volume including Short Communications and Posters is published on CD. The COMPSTAT 2002 is the 15th conference in a serie of biannual conferences with the objective to present the latest developments in Computational Statistics and is taking place from August 24th to August 28th, 2002. Previous COMPSTATs were in Vienna (1974), Berlin (1976), Leiden (1978), Edinburgh (1980), Toulouse (1982), Pra~ue (1984), Rome (1986), Copenhagen (1988), Dubrovnik (1990), Neuchatel (1992), Vienna (1994), Barcelona (1996), Bris tol (1998) and Utrecht (2000). COMPSTAT 2002 is organise...
Complexity of Social Phenomena
What is complexity? What are the characteristics of a complex system? What does it mean to measure? How to develop an efficient tool for measuring socio-economic phenomena? What is synthesis? What are the main statistical tools for synthesis of multi-indicator systems? These are the research questions that are attempted to be answered in this book, the result of the author’s research work during his PhD. The book offers a conceptual and methodological analysis of the topic of synthesis of complex social phenomena, also proposing interesting applications to real cases. Winner of the Competition “Prize for PhD Thesis 2020” arranged by Sapienza University Press.
Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives
Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives An insightful treatment of present and emerging technologies in fault diagnosis and failure prognosis In Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives, a team of distinguished researchers delivers a comprehensive exploration of current and emerging approaches to fault diagnosis and failure prognosis of electrical machines and drives. The authors begin with foundational background, describing the physics of failure, the motor and drive designs and components that affect failure and signals, signal processing, and analysis. The book then moves on to describe the features of these sig...
Cyber Crime: Concepts, Methodologies, Tools and Applications
Threatening the safety of individuals, computers, and entire networks, cyber crime attacks vary in severity and type. Studying this continually evolving discipline involves not only understanding different types of attacks, which range from identity theft to cyberwarfare, but also identifying methods for their prevention. Cyber Crime: Concepts, Methodologies, Tools and Applications is a three-volume reference that explores all aspects of computer-based crime and threats, offering solutions and best practices from experts in software development, information security, and law. As cyber crime continues to change and new types of threats emerge, research focuses on developing a critical understanding of different types of attacks and how they can best be managed and eliminated.
Online Consumer Protection: Theories of Human Relativism
Presents a broad range of international findings in online consumer protection. Covers the nature of online threats, consumer concerns, and techniques for online privacy protection.
COMPSTAT 2006 - Proceedings in Computational Statistics
International Association for Statistical Computing The International Association for Statistical Computing (IASC) is a Section of the International Statistical Institute. The objectives of the Association are to foster world-wide interest in e?ective statistical computing and to - change technical knowledge through international contacts and meetings - tween statisticians, computing professionals, organizations, institutions, g- ernments and the general public. The IASC organises its own Conferences, IASC World Conferences, and COMPSTAT in Europe. The 17th Conference of ERS-IASC, the biennial meeting of European - gional Section of the IASC was held in Rome August 28 - September 1, 2006. Th...
COMPSTAT
Proceedings of symposia held 1974-
Time Series Clustering and Classification
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
