Knowledge-Based Clustering

This is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to explore: proximity measures; hierarchical clustering; partition clustering; neural network-based clustering; kernel-based clustering; sequential data clustering; large-scale data clustering; data visualization and high-dimensional data clustering; and cluster validation. The authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds.

Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixtures adapted to different types of data. The algorithms used are described and related works with different classical methods are presented and commented upon. This chapter is useful in tackling the problem of co-clustering under the mixture approach. Chapter 2 is devoted to the latent block model proposed in the mixture approach context. The authors discuss this model in detail and present its interest regarding co-clustering. Various algorithms are presented in a general context. Chapter 3 focuses on binary and categorical data. It presents, in detail, the appropriated latent block mixture models. Variants of these models and algorithms are presented and illustrated using examples. Chapter 4 focuses on contingency data. Mutual information, phi-squared and model-based co-clustering are studied. Models, algorithms and connections among different approaches are described and illustrated. Chapter 5 presents the case of continuous data. In the same way, the different approaches used in the previous chapters are extended to this situation. Contents 1. Cluster Analysis. 2. Model-Based Co-Clustering. 3. Co-Clustering of Binary and Categorical Data. 4. Co-Clustering of Contingency Tables. 5. Co-Clustering of Continuous Data. About the Authors G?rard Govaert is Professor at the University of Technology of Compi?gne, France. He is also a member of the CNRS Laboratory Heudiasyc (Heuristic and diagnostic of complex systems). His research interests include latent structure modeling, model selection, model-based cluster analysis, block clustering and statistical pattern recognition. He is one of the authors of the MIXMOD (MIXtureMODelling) software. Mohamed Nadif is Professor at the University of Paris-Descartes, France, where he is a member of LIPADE (Paris Descartes computer science laboratory) in the Mathematics and Computer Science department. His research interests include machine learning, data mining, model-based cluster analysis, co-clustering, factorization and data analysis. Cluster Analysis is an important tool in a variety of scientific areas. Chapter 1 briefly presents a state of the art of already well-established as well more recent methods. The hierarchical, partitioning and fuzzy approaches will be discussed amongst others. The authors review the difficulty of these classical methods in tackling the high dimensionality, sparsity and scalability. Chapter 2 discusses the interests of coclustering, presenting different approaches and defining a co-cluster. The authors focus on co-clustering as a simultaneous clustering and discuss the cases of binary, continuous and co-occurrence data. The criteria and algorithms are described and illustrated on simulated and real data. Chapter 3 considers co-clustering as a model-based co-clustering. A latent block model is defined for different kinds of data. The estimation of parameters and co-clustering is tackled under two approaches: maximum likelihood and classification maximum likelihood. Hard and soft algorithms are described and applied on simulated and real data. Chapter 4 considers co-clustering as a matrix approximation. The trifactorization approach is considered and algorithms based on update rules are described. Links with numerical and probabi

There is an increasing need throughout the biomedical sciences for a greater understanding of knowledge-based systems and their application to genomic and proteomic research. This book discusses knowledge-based and statistical approaches, along with applications in bioinformatics and systems biology. The text emphasizes the integration of different methods for analysing and interpreting biomedical data. This, in turn, can lead to breakthrough biomolecular discoveries, with applications in personalized medicine. Key Features: Explores the fundamentals and applications of knowledge-based and statistical approaches in bioinformatics and systems biology. Helps readers to interpret genomic, proteomic, and metabolomic data in understanding complex biological molecules and their interactions. Provides useful guidance on dealing with large datasets in knowledge bases, a common issue in bioinformatics. Written by leading international experts in this field. Students, researchers, and industry professionals with a background in biomedical sciences, mathematics, statistics, or computer science will benefit from this book. It will also be useful for readers worldwide who want to master the application of bioinformatics to real-world situations and understand biological problems that motivate algorithms.

Discover the technology for the next generation of radar systems Here is the first book that brings together the key concepts essential for the application of Knowledge Based Systems (KBS) to radar detection, tracking, classification, and scheduling. The book highlights the latest advances in both KBS and radar signal and data processing, presenting a range of perspectives and innovative results that have set the stage for the next generation of adaptive radar systems. The book begins with a chapter introducing the concept of Knowledge Based (KB) radar. The remaining nine chapters focus on current developments and recent applications of KB concepts to specific radar functions. Among the key topics explored are: Fundamentals of relevant KB techniques KB solutions as they apply to the general radar problem KBS applications for the constant false-alarm rate processor KB control for space-time adaptive processing KB techniques applied to existing radar systems Integrated end-to-end radar signals Data processing with overarching KB control All chapters are self-contained, enabling readers to focus on those topics of greatest interest. Each one begins with introductory remarks, moves on to detailed discussions and analysis, and ends with a list of references. Throughout the presentation, the authors offer examples of how KBS works and how it can dramatically improve radar performance and capability. Moreover, the authors forecast the impact of KB technology on future systems, including important civilian, military, and homeland defense applications. With chapters contributed by leading international researchers and pioneers in the field, this text is recommended for both students and professionals in radar and sonar detection, tracking, and classification and radar resource management.

Multidisciplinary Design Optimization supported by Knowledge Based Engineering supports engineers confronting this daunting and new design paradigm. It describes methodology for conducting a system design in a systematic and rigorous manner that supports human creativity to optimize the design objective(s) subject to constraints and uncertainties. The material presented builds on decades of experience in Multidisciplinary Design Optimization (MDO) methods, progress in concurrent computing, and Knowledge Based Engineering (KBE) tools. Key features: Comprehensively covers MDO and is the only book to directly link this with KBE methods Provides a pathway through basic optimization methods to MDO methods Directly links design optimization methods to the massively concurrent computing technology Emphasizes real world engineering design practice in the application of optimization methods Multidisciplinary Design Optimization supported by Knowledge Based Engineering is a one-stop-shop guide to the state-of-the-art tools in the MDO and KBE disciplines for systems design engineers and managers. Graduate or post-graduate students can use it to support their design courses, and researchers or developers of computer-aided design methods will find it useful as a wide-ranging reference.

Managing Change in Construction Projects: a knowledge-based approach offers a new perspective on construction project change by viewing the process of change management as a knowledge-intensive activity, where team members bring their tacit and explicit knowledge into the situation; share, create and capture this collective knowledge for future re-use in similar situations. Through this knowledge-based approach, construction teams can successfully resolve and learn from change events, leading to an overall improved performance of the industry. The book will make a significant contribution to our understanding of construction project change by offering new theoretical and practical insights and models grounded in results of case studies conducted within two collaborative construction project team settings. By demonstrating how the social construction of knowledge works in construction settings, the authors challenge the prevailing change management solutions based on ‘hard’ IT approaches. They put forward a balanced view that incorporates both IT–based and socially constructed approaches to effective management of construction project change. helps construction managers to improve and learn through the process of construction project change presents new theoretical models and offers practical guidelines first research-based book to directly address project change from a knowledge-based perspective draws on detailed studies with construction companies, including Ballast Construction and Kier Construction encourages a move from the information driven, process integrated approach to a knowledge-based view

Praise for The Handbook ofKnowledge-Based Coaching «Definitive, with extensive references and a commitment to connecting theory to practice in every chapter, this important contribution is a delicious and wide-ranging exploration of the lineages that have shaped the modern practice of coaching.» —Doug Silsbee, author, Presence-Based Coaching and The Mindful Coach «The translation of theories from multiple disciplines to the practice of coaching makes this book a must-read!» —Terrence E. Maltbia, senior lecturer, Adult Learning and Leadership; and faculty director, Columbia Coaching Certification Program, Teachers College, Columbia University «If you have an appetite for the scientific roots of what works best in coaching, and you are hungry for an easy-to-digest translation of the science to practice, this book is a feast and will be on your plate for many years to come.» —Margaret Moore (Coach Meg), founder and CEO, Wellcoaches Corporation; and codirector, Institute of Coaching, McLean Hospital, Harvard Medical School «Whether you're a beginner or an experienced coach, this rollicking ride through dozens of the most important theories and perspectives in coaching will be a vital companion. With quick and helpful summaries of key ideas and their use—and selective bibliographies should you wish to go deeper into a particular area—this book will help you support your clients in a targeted and sophisticated way.» —Jennifer Garvey Berger, author, Changing on the Job: Growing the Leaders Our Organizations Need; and coeditor, Executive Coaching: Practices and Perspectives «This is a book I have been missing. What a pleasure to read and what a stretching of my mind.» —Kim G?rtz, senior consultant, Copenhagen Coaching Center «Anyone who is serious about improving the quality of coaching will find The Handbook an invaluable resource that reflects the breadth and richness of the growing evidence-based approach to coaching practice.» —David Clutterbuck, visiting professor in the coaching and mentoring faculties, Oxford Brookes and Sheffield Hallam Universities

Provides an overview of the developments and advances in the field of network clustering and blockmodeling over the last 10 years This book offers an integrated treatment of network clustering and blockmodeling, covering all of the newest approaches and methods that have been developed over the last decade. Presented in a comprehensive manner, it offers the foundations for understanding network structures and processes, and features a wide variety of new techniques addressing issues that occur during the partitioning of networks across multiple disciplines such as community detection, blockmodeling of valued networks, role assignment, and stochastic blockmodeling. Written by a team of international experts in the field, Advances in Network Clustering and Blockmodeling offers a plethora of diverse perspectives covering topics such as: bibliometric analyses of the network clustering literature; clustering approaches to networks; label propagation for clustering; and treating missing network data before partitioning. It also examines the partitioning of signed networks, multimode networks, and linked networks. A chapter on structured networks and coarsegrained descriptions is presented, along with another on scientific coauthorship networks. The book finishes with a section covering conclusions and directions for future work. In addition, the editors provide numerous tables, figures, case studies, examples, datasets, and more. Offers a clear and insightful look at the state of the art in network clustering and blockmodeling Provides an excellent mix of mathematical rigor and practical application in a comprehensive manner Presents a suite of new methods, procedures, algorithms for partitioning networks, as well as new techniques for visualizing matrix arrays Features numerous examples throughout, enabling readers to gain a better understanding of research methods and to conduct their own research effectively Written by leading contributors in the field of spatial networks analysis Advances in Network Clustering and Blockmodeling is an ideal book for graduate and undergraduate students taking courses on network analysis or working with networks using real data. It will also benefit researchers and practitioners interested in network analysis.

There is a strong movement today in management to encourage management practices based on research evidence. In the first volume of this handbook, I asked experts in 39 areas of management to identify a central principle that summarized and integrated the core findings from their specialty area and then to explain this principle and give real business examples of the principle in action. I asked them to write in non-technical terms, e.g., without a lot of statistics, and almost all did so. The previous handbook proved to be quite popular, so I was asked to edit a second edition. This new edition has been expanded to 33 topics, and there are some new authors for the previously included topics. The new edition also includes: updated case examples, updated references and practical exercises at the end of each chapter. It also includes a preface on evidence-based management. The principles for the first edition were intended to be relatively timeless, so it is no surprise that most of the principles are the same (though some chapter titles include more than one principle). This book could serve as a textbook in advanced undergraduate and in MBA courses. It could also be of use to practicing managers and not just those in Human Resource departments. Every practicing manager may not want to read the whole book, but I am willing to guarantee that every one will find at least one or more chapters that will be practically useful. In this time of economic crisis, the need for effective management practices is more acute than ever.