This book focuses on the theory, practice, and concepts of process mining techniques in detail, especially pattern recognition in diverse society, science, medicine, engineering, and business. The book deliberates several perspectives on process mining techniques in the broader context of data science and big data approaches. Process Mining Techniques for Pattern Recognition: Concepts, Theory, and Practice provides an introduction to process mining techniques and pattern recognition. After that, it delivers the fundamentals of process modelling and mining essential to comprehend the book. The text emphasizes discovery as an important process mining task and includes case studies as well as real-life examples to guide users in successfully applying process mining techniques for pattern recognition in practice. Intended to be an introduction to process mining and pattern recognition for students, academics, and practitioners, this book is perfect for those who want to learn the basics, and also gain an understanding of the concepts on a deeper level.

After a brief presentation of the state of the art of process-mining techniques, Andrea Burratin proposes different scenarios for the deployment of process-mining projects, and in particular a characterization of companies in terms of their process awareness. The approaches proposed in this book belong to two different computational paradigms: first to classic "batch process mining," and second to more recent "online process mining." The book encompasses a revised version of the author's PhD thesis, which won the "Best Process Mining Dissertation Award" in 2014, awarded by the IEEE Task Force on Process Mining.

What are the possibilities for process mining in hospitals? In this book the authors provide an answer to this question by presenting a healthcare reference model that outlines all the different classes of data that are potentially available for process mining in healthcare and the relationships between them. Subsequently, based on this reference model, they explain the application opportunities for process mining in this domain and discuss the various kinds of analyses that can be performed. They focus on organizational healthcare processes rather than medical treatment processes. The combination of event data and process mining techniques allows them to analyze the operational processes within a hospital based on facts, thus providing a solid basis for managing and improving processes within hospitals. To this end, they also explicitly elaborate on data quality issues that are relevant for the data aspects of the healthcare reference model. This book mainly targets advanced professionals involved in areas related to business process management, business intelligence, data mining, and business process redesign for healthcare systems as well as graduate students specializing in healthcare information systems and process analysis.

There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.

Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application.Matrix Methods in Data Mining and Pattern Recognition is divided into three parts. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB®. Some mathematical proofs that emphasize the existence and properties of the matrix decompositions are included. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms. The applications discussed by the author are: classification of handwritten digits, text mining, text summarization, pagerank computations related to the GoogleÔ search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.Audience The book is intended for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course. Graduate students in various data mining and pattern recognition areas who need an introduction to linear algebra techniques will also find the book useful.Contents Preface; Part I: Linear Algebra Concepts and Matrix Decompositions. Chapter 1: Vectors and Matrices in Data Mining and Pattern Recognition; Chapter 2: Vectors and Matrices; Chapter 3: Linear Systems and Least Squares; Chapter 4: Orthogonality; Chapter 5: QR Decomposition; Chapter 6: Singular Value Decomposition; Chapter 7: Reduced-Rank Least Squares Models; Chapter 8: Tensor Decomposition; Chapter 9: Clustering and Nonnegative Matrix Factorization; Part II: Data Mining Applications. Chapter 10: Classification of Handwritten Digits; Chapter 11: Text Mining; Chapter 12: Page Ranking for a Web Search Engine; Chapter 13: Automatic Key Word and Key Sentence Extraction; Chapter 14: Face Recognition Using Tensor SVD. Part III: Computing the Matrix Decompositions. Chapter 15: Computing Eigenvalues and Singular Values; Bibliography; Index.

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

This book is a revised version of the PhD dissertation written by the author at the Department of Business Informatics and Operations Management at Ghent University in Belgium. It addresses shortcomings in Business Process Management concerning loosely framed knowledge-intensive processes, which are characterized by their numerous valid process variants and their reliance on knowledge workers to apply their knowledge to decide on a suitable process variant that fits the context of a specific process execution. The goal was to lay the foundation for a process-aware business process management (IT-)system to support such processes. Several proof-of-concept implementations have been made for the core components and were evaluated in the domain of the healthcare. Starting from an artificial, but realistic, case about patients that arrive in the emergency room with suspected arm fractures and later progressing to a case study of the diagnosis and treatment of patients in the emergency department of a real hospital, using data from their patient files. In 2020, the PhD dissertation won the “CAiSE PhD award”, granted to outstanding PhD theses in the field of Information Systems Engineering.

Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state space-based systems for process monitoring, control and diagnosis. The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.

This volume presents 70 carefully selected papers from a major joint event: the 8th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016) and the 8th International Conference on Computational Aspects of Social Networks (CASoN 2016). SoCPaR–CASoN 2016, which was organized by the Machine Intelligence Research Labs (MIR Labs), USA and Vellore Institute of Technology (VIT), India and held at the VIT on December 19–21, 2016. It brings together researchers and practitioners from academia and industry to share their experiences and exchange new ideas on all interdisciplinary areas of soft computing and pattern recognition, as well as intelligent methods applied to social networks. This book is a valuable resource for practicing engineers/scientists and researchers working in the field of soft computing, pattern recognition and social networks.

Life expectancy is increasing, and we are all expected to work for longer as a result. A balance must be found between the demands of work and human capabilities, and this makes the prevention of workplace-related health problems more important than ever. Emerging technologies, such as smart textiles, wearable devices, and the Internet of Things have enabled the development of intelligent biomedical clothing and the integration of pervasive sensitive services into the environment, and together with ambient intelligence technology techniques and big data analytics, have fostered a proliferation of p-Health monitoring solutions. This book presents a collection of the most significant challenges and advances in the field of intelligent workspaces and personalized ergonomics, bringing together the most relevant results of various international research projects. The book is organized into three main sections: Personalized Ergonomics, which explores the need for practical and reliable risk assessment methods for the prevention of musculoskeletal disorders and the enhancement of the workplace; Pervasive Technology for Intelligent Workplaces, which identifies the opportunities and challenges of technology-based interventions and the security and privacy issues of the smart workplace; and Data Warehouse Governance and Analytics. The book concludes with a chapter on lessons learnt. The transformation of the working environment into a healthy and intelligent space will not only support ergonomists, employees and employers, but may also be the solution to the sustainability of our current social welfare systems, and the book will be of interest to all those concerned with workplace health.