Clustering Challenges In Biological Networks
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Clustering Challenges in Biological Networks
Author | : Sergiy Butenko,W. Art Chaovalitwongse,Panos M. Pardalos |
Publsiher | : World Scientific |
Total Pages | : 347 |
Release | : 2009 |
Genre | : Science |
ISBN | : 9789812771650 |
Download Clustering Challenges in Biological Networks Book in PDF, Epub and Kindle
This text offers introductory knowledge of a wide range of clustering and other quantitative techniques used to solve biological problems.
Summarizing Biological Networks
Author | : Sourav S. Bhowmick,Boon-Siew Seah |
Publsiher | : Springer |
Total Pages | : 146 |
Release | : 2017-04-17 |
Genre | : Computers |
ISBN | : 9783319546216 |
Download Summarizing Biological Networks Book in PDF, Epub and Kindle
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.
Recent Advances in Biological Network Analysis
Author | : Byung-Jun Yoon,Xiaoning Qian |
Publsiher | : Springer Nature |
Total Pages | : 220 |
Release | : 2021-01-13 |
Genre | : Medical |
ISBN | : 9783030571733 |
Download Recent Advances in Biological Network Analysis Book in PDF, Epub and Kindle
This book reviews recent advances in the emerging field of computational network biology with special emphasis on comparative network analysis and network module detection. The chapters in this volume are contributed by leading international researchers in computational network biology and offer in-depth insight on the latest techniques in network alignment, network clustering, and network module detection. Chapters discuss the advantages of the respective techniques and present the current challenges and open problems in the field. Recent Advances in Biological Network Analysis: Comparative Network Analysis and Network Module Detection will serve as a great resource for graduate students, academics, and researchers who are currently working in areas relevant to computational network biology or wish to learn more about the field. Data scientists whose work involves the analysis of graphs, networks, and other types of data with topological structure or relations can also benefit from the book's insights.
Data Clustering
Author | : Charu C. Aggarwal,Chandan K. Reddy |
Publsiher | : CRC Press |
Total Pages | : 652 |
Release | : 2016-04-08 |
Genre | : Business & Economics |
ISBN | : 9781498785778 |
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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.
Transactions on Large Scale Data and Knowledge Centered Systems XXXIX
Author | : Abdelkader Hameurlain,Roland Wagner,Djamal Benslimane,Ernesto Damiani,William I. Grosky |
Publsiher | : Springer |
Total Pages | : 227 |
Release | : 2018-11-22 |
Genre | : Computers |
ISBN | : 9783662584156 |
Download Transactions on Large Scale Data and Knowledge Centered Systems XXXIX Book in PDF, Epub and Kindle
This, the 39th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of seven papers selected from the 37 contributions presented at the 28th International Conference on Database and Expert Systems Applications, DEXA 2017, held in Lyon, France, in August 2017. Topics covered include knowledge bases, clustering algorithms, parallel frequent itemset mining, model-driven engineering, virtual machines, recommendation systems, and federated SPARQL query processing.
Unsupervised Learning Algorithms
Author | : M. Emre Celebi,Kemal Aydin |
Publsiher | : Springer |
Total Pages | : 558 |
Release | : 2016-04-29 |
Genre | : Technology & Engineering |
ISBN | : 9783319242118 |
Download Unsupervised Learning Algorithms Book in PDF, Epub and Kindle
This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest include anomaly detection, clustering, feature extraction, and applications of unsupervised learning. Each chapter is contributed by a leading expert in the field.
Theory and Applications of Models of Computation
Author | : Manindra Agrawal,Ding-Zhu Du,Zhenhua Duan,Angsheng Li |
Publsiher | : Springer |
Total Pages | : 598 |
Release | : 2008-04-30 |
Genre | : Computers |
ISBN | : 9783540792284 |
Download Theory and Applications of Models of Computation Book in PDF, Epub and Kindle
This proceedings volume examines all major areas in computer science, mathematics (especially logic) and the physical sciences, especially computation, algorithms, complexity and computability theory.
Learning and Intelligent Optimization
Author | : Nikolaos F. Matsatsinis,Yannis Marinakis,Panos Pardalos |
Publsiher | : Springer Nature |
Total Pages | : 412 |
Release | : 2020-01-21 |
Genre | : Mathematics |
ISBN | : 9783030386290 |
Download Learning and Intelligent Optimization Book in PDF, Epub and Kindle
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.