This text offers introductory knowledge of a wide range of clustering and other quantitative techniques used to solve biological problems.

This book focuses on the data mining, systems biology, and bioinformatics computational methods that can be used to summarize biological networks. Specifically, it discusses an array of techniques related to biological network clustering, network summarization, and differential network analysis which enable readers to uncover the functional and topological organization hidden in a large biological network. The authors also examine crucial open research problems in this arena. Academics, researchers, and advanced-level students will find this book to be a comprehensive and exceptional resource for understanding computational techniques and their applications for a summary of biological networks.

The 4th Annual International Conference on Combinatorial Optimization and Applications (COCOA 2010) took place in Big Island, Hawaii, USA, December 18–20, 2010. Past COCOA conferences were held in Xi’an, China (2007), Newfoundland, Canada (2008)and Huangshan, China (2009). COCOA2010providedaforumforresearchersworkingintheareasofcom- natorial optimization and its applications. In addition to theoretical results, the conference also included recent works on experimental and applied research of general algorithmic interest. The Program Committee received 108 submissions from more than 23 countries and regions, including Australia, Austria, Canada, China, Denmark, France, Germany, Hong Kong, India, Italy, Japan, Korea, Mexico, New Zealand, Poland, Slovak Republic, Spain, Sweden, Switzerland, Taiwan, UK, USA, Vietnam, etc. Among the 108 submissions, 49 regular papers were selected for presentation at the conference and are included in this volume. Some of these papers will be selected for publication in a special issue of the Journal of Combinatorial Optimization, a special issue of Theoretical Computer Science, a special issue of Optimization Letters, and a special issue of Discrete Mathematics, Algorithms and Applications under the standard refereeing procedure.

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.

This volume includes contributions from diverse disciplines including electrical engineering, biomedical engineering, industrial engineering, and medicine, bridging a vital gap between the mathematical sciences and neuroscience research. Covering a wide range of research topics, this volume demonstrates how various methods from data mining, signal processing, optimization and cutting-edge medical techniques can be used to tackle the most challenging problems in modern neuroscience.

This book constitutes the thoroughly refereed pChania, Crete, Greece, in May 2019. The 38 full papers presented have been carefully reviewed and selected from 52 submissions. The papers focus on advancedresearch developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence and describe advanced ideas, technologies, methods, and applications in optimization and machine learning.

As the age of Big Data emerges, it becomes necessary to take the five dimensions of Big Data- volume, variety, velocity, volatility, and veracity- and focus these dimensions towards one critical emphasis - value. The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualization, interdisciplinary communication, and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal.

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networks—measures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.

This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2013, held in Nice, France in June 2013. The 19 revised full papers presented were carefully reviewed and selected from 35 submissions. The papers are organized in topical sections on bioinformatics, biostatistics, knowledge based medicine, and data integration and analysis in omic-science.

This volume constitutes the refereed proceedings of the 9th International Symposium on Experimental Algorithms, SEA 2010, held on Ischia Island, Naples, Italy, in May 2010. The 40 revised full papers presented together with two invited papers were carefully reviewed and selected from 73 submissions. The topics covered include algorithm engineering, algorithmic libraries, algorithmic mechanism design, analysis of algorithms, algorithms for memory hierarchies, approximation techniques, bioinformatics, branch and bound algorithms, combinatorial and irregular problems, combinatorial structures and graphs, communication networks, complex networks, computational geometry, computational learning theory, computational optimization, computer systems, cryptography and security, data streams, data structures, distributed and parallel algorithms, evaluation of algorithms for realistic environments, experimental techniques and statistics, graph drawing, heuristics for combinatorial optimization