Download or read book Evolutionary Spectral Co clustering written by Nathan S. Green and published by . This book was released on 2010 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The field of mining evolving data is relatively new and evolutionary clustering is among the latest in this trend. Presently, there are algorithms for evolutionary k-means, agglomerative hierarchical, and spectral clustering. These have been excellent in showing the advantages of using evolving data snapshots for better clustering results. From these algorithms the key portion of the conversion from static data handling to evolving data handling has been the addition of the historical cost function. The cost function is what determines whether or not instances should be moved from one cluster to the next between time-steps based on the historical cuts made between the instances in the dataset. These cost functions are then the method by which evolutionary clustering provides smooth transitions as there is a tunable tolerance for shifts in cluster membership. This also means that transitions between clusters become much more significant. For example, if an author-word matrix were clustered over ten years and an author changed clusters part way through the time-line it is a likely indicator that the author has changed research topics. Methods for mining evolving data have not yet expanded into co-clustering; for this reason I have contributed a new algorithm for co-clustering evolving data. The algorithm uses spectral co-clustering to cluster each time-step of instances and features. Using the previous example, cluster changes in features (or words) for an author-word matrix is significant in that it may indicate a change in meaning for the word. This contribution to the field provides an avenue for further development of evolutionary co-clustering algorithms."--Abstract.
Download or read book Evolutionary Star structured Heterogeneous Data Co clustering written by Amit M. Salunke and published by . This book was released on 2012 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: "A star-structured interrelationship, which is a more common type in real world data, has a central object connected to the other types of objects. One of the key challenges in evolutionary clustering is integration of historical data in current data. Traditionally, smoothness in data transition over a period of time is achieved by means of cost functions defined over historical and current data. These functions provide a tunable tolerance for shifts of current data accounting instance to all historical information for corresponding instance. Once historical data is integrated into current data using cost functions, co-clustering is obtained using various co-clustering algorithms like spectral clustering, non-negative matrix factorization, and information theory based clustering. Non-negative matrix factorization has been proven efficient and scalable for large data and is less memory intensive compared to other approaches. Non-negative matrix factorization tri-factorizes original data matrix into row indicator matrix, column indicator matrix, and a matrix that provides correlation between the row and column clusters. However, challenges in clustering evolving heterogeneous data have never been addressed. In this thesis, I propose a new algorithm for clustering a specific case of this problem, viz. the star-structured heterogeneous data. The proposed algorithm will provide cost functions to integrate historical star-structured heterogeneous data into current data. Then I will use non-negative matrix factorization to cluster each time-step of instances and features. This contribution to the field will provide an avenue for further development of higher order evolutionary co-clustering algorithms."--Abstract.
Download or read book Semi supervised Heterogeneous Evolutionary Co clustering written by Pankaj Andhale and published by . This book was released on 2012 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: "One of the challenges of the machine learning problem is the absence of sufficient number of labeled instances or training instances. At the same time generating labeled data is expensive and time consuming. The semi-supervised approach has shown promising results to solve the problem of insufficient or fewer labeled instance datasets. The key challenge is incorporating the semi-supervised knowledge into the heterogeneous data which is evolving in nature. Most of the prior work that uses semi-supervised knowledge has been performed on heterogeneous static data. The semi-supervised knowledge is incorporated into data which aid the clustering algorithm to obtain better clusters. The semi-supervised knowledge is provided as constrained based or distance based. I am proposing a framework to incorporate prior knowledge to perform co-clustering on the evolving heterogeneous data. This framework can be used to solve a wide range of problems dealing with text analysis, web analysis and image grouping. In the semi-supervised approach we incorporate the domain knowledge by placing the constraints which aid the clustering process in performing effective clustering of the data. In the proposed framework, I am using the constraint based semi-supervised non-negative matrix factorization approach to obtain the co-clustering on the heterogeneous evolving data. The constraint based semi-supervised approach uses the user provided must-link or cannot-link constraints on the central data type before performing co-clustering. To process the original datasets efficiently in terms of time and space I am using the low rank approximation technique to obtain the sparse representation of the input data matrix using the Dynamic Colibri approach."--Abstract.
Download or read book Evolutionary Data Clustering Algorithms and Applications written by Ibrahim Aljarah and published by Springer Nature. This book was released on 2021-02-20 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an in-depth analysis of the current evolutionary clustering techniques. It discusses the most highly regarded methods for data clustering. The book provides literature reviews about single objective and multi-objective evolutionary clustering algorithms. In addition, the book provides a comprehensive review of the fitness functions and evaluation measures that are used in most of evolutionary clustering algorithms. Furthermore, it provides a conceptual analysis including definition, validation and quality measures, applications, and implementations for data clustering using classical and modern nature-inspired techniques. It features a range of proven and recent nature-inspired algorithms used to data clustering, including particle swarm optimization, ant colony optimization, grey wolf optimizer, salp swarm algorithm, multi-verse optimizer, Harris hawks optimization, beta-hill climbing optimization. The book also covers applications of evolutionary data clustering in diverse fields such as image segmentation, medical applications, and pavement infrastructure asset management.
Download or read book Relational Data Clustering written by Bo Long and published by CRC Press. This book was released on 2010-05-19 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems. After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering: Clustering on bi-type heterogeneous relational data Multi-type heterogeneous relational data Homogeneous relational data clustering Clustering on the most general case of relational data Individual relational clustering framework Recent research on evolutionary clustering This book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.
Download or read book Mathematical Analysis of Evolution Information and Complexity written by Wolfgang Arendt and published by John Wiley & Sons. This book was released on 2009-07-10 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical Analysis of Evolution, Information, and Complexity deals with the analysis of evolution, information and complexity. The time evolution of systems or processes is a central question in science, this text covers a broad range of problems including diffusion processes, neuronal networks, quantum theory and cosmology. Bringing together a wide collection of research in mathematics, information theory, physics and other scientific and technical areas, this new title offers elementary and thus easily accessible introductions to the various fields of research addressed in the book.
Download or read book Data Clustering written by Charu C. Aggarwal and published by CRC Press. This book was released on 2018-09-03 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Spectral Clustering and Biclustering written by Marianna Bolla and published by John Wiley & Sons. This book was released on 2013-06-27 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explores regular structures in graphs and contingency tables by spectral theory and statistical methods This book bridges the gap between graph theory and statistics by giving answers to the demanding questions which arise when statisticians are confronted with large weighted graphs or rectangular arrays. Classical and modern statistical methods applicable to biological, social, communication networks, or microarrays are presented together with the theoretical background and proofs. This book is suitable for a one-semester course for graduate students in data mining, multivariate statistics, or applied graph theory; but by skipping the proofs, the algorithms can also be used by specialists who just want to retrieve information from their data when analysing communication, social, or biological networks. Spectral Clustering and Biclustering: Provides a unified treatment for edge-weighted graphs and contingency tables via methods of multivariate statistical analysis (factoring, clustering, and biclustering). Uses spectral embedding and relaxation to estimate multiway cuts of edge-weighted graphs and bicuts of contingency tables. Goes beyond the expanders by describing the structure of dense graphs with a small spectral gap via the structural eigenvalues and eigen-subspaces of the normalized modularity matrix. Treats graphs like statistical data by combining methods of graph theory and statistics. Establishes a common outline structure for the contents of each algorithm, applicable to networks and microarrays, with unified notions and principles.
Download or read book Modern Statistical Methods for Health Research written by Yichuan Zhao and published by Springer Nature. This book was released on 2021-10-14 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together the voices of leading experts in the frontiers of biostatistics, biomedicine, and the health sciences to discuss the statistical procedures, useful methods, and novel applications in biostatistics research. It also includes discussions of potential future directions of biomedicine and new statistical developments for health research, with the intent of stimulating research and fostering the interactions of scholars across health research related disciplines. Topics covered include: Health data analysis and applications to EHR data Clinical trials, FDR, and applications in health science Big network analytics and its applications in GWAS Survival analysis and functional data analysis Graphical modelling in genomic studies The book will be valuable to data scientists and statisticians who are working in biomedicine and health, other practitioners in the health sciences, and graduate students and researchers in biostatistics and health.
Download or read book Link Mining Models Algorithms and Applications written by Philip S. Yu and published by Springer Science & Business Media. This book was released on 2010-09-16 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers detailed surveys and systematic discussion of models, algorithms and applications for link mining, focusing on theory and technique, and related applications: text mining, social network analysis, collaborative filtering and bioinformatics.
Download or read book Smart Applications and Data Analysis written by Mohamed Hamlich and published by Springer Nature. This book was released on 2020-06-04 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes refereed proceedings of the Third International Conference on Smart Applications and Data Analysis, SADASC 2020, held in Marrakesh, Morocco. Due to the COVID-19 pandemic the conference has been postponed to June 2020. The 24 full papers and 3 short papers presented were thoroughly reviewed and selected from 44 submissions. The papers are organized according to the following topics: ontologies and meta modeling; cyber physical systems and block-chains; recommender systems; machine learning based applications; combinatorial optimization; simulations and deep learning.
Download or read book Advances in Metal and Semiconductor Clusters written by M.A. Duncan and published by Elsevier. This book was released on 1998-07-27 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster Materials is the fourth volume of the highly successful series Advances in Metal and Semiconductor Clusters. In this volume the focus is on the properties of clusters which determine their potential applications as new materials. Metal and semiconductor clusters have been proposed as precursors for materials or as actual materials since the earliest days of cluster research. In the last few years, a variety of techniques have made it possible to produce clusters in sizes varying from a few atoms up to several thousand atoms. While some measurements are performed in the gas phase on non-isolated clusters, many cluster materials can now be isolated in macroscopic quantities and more convenient studies of their properties become possible. In this volume the authors focus on measurement of optical, electronic, magnetic, chemical and mechanical properties of clusters or of cluster assemblies. All of these properties must fall into acceptable ranges of behaviour before useful materials composed of clusters can be put into practical applications. As evidenced by the various work described here, the realisation of practical products based on cluster materials seems to be approaching rapidly.
Download or read book Spectral Algorithms written by Ravindran Kannan and published by Now Publishers Inc. This book was released on 2009 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spectral methods refer to the use of eigenvalues, eigenvectors, singular values and singular vectors. They are widely used in Engineering, Applied Mathematics and Statistics. More recently, spectral methods have found numerous applications in Computer Science to "discrete" as well as "continuous" problems. Spectral Algorithms describes modern applications of spectral methods, and novel algorithms for estimating spectral parameters. The first part of the book presents applications of spectral methods to problems from a variety of topics including combinatorial optimization, learning and clustering. The second part of the book is motivated by efficiency considerations. A feature of many modern applications is the massive amount of input data. While sophisticated algorithms for matrix computations have been developed over a century, a more recent development is algorithms based on "sampling on the fly" from massive matrices. Good estimates of singular values and low rank approximations of the whole matrix can be provably derived from a sample. The main emphasis in the second part of the book is to present these sampling methods with rigorous error bounds. It also presents recent extensions of spectral methods from matrices to tensors and their applications to some combinatorial optimization problems.
Download or read book Evolution of Size Effects in Chemical Dynamics Volume 70 Part 2 written by Stuart A. Rice and published by John Wiley & Sons. This book was released on 2009-09-08 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Advances in Chemical Physics series provides the chemical physics and physical chemistry fields with a forum for critical, authoritative evaluations of advances in every area of the discipline. Filled with cutting-edge research reported in a cohesive manner not found elsewhere in the literature, each volume of the Advances in Chemical Physics series serves as the perfect supplement to any advanced graduate class devoted to the study of chemical physics.
Download or read book Dynamical Evolution of Dense Stellar Systems IAU S246 written by International Astronomical Union. Symposium and published by Cambridge University Press. This book was released on 2008-06-12 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dense stellar systems lie at the interface between dynamics, stellar evolution, and galaxy formation, and they provide us with an ideal laboratory to understand many different aspects of these important fields as well as to explore the interplay between them. The complete study of dense stellar systems is a very challenging task which requires the collaboration and the exchange of ideas of astronomers and physicists with observational and theoretical expertise in galactic and extra-galactic astronomy, stellar dynamics, hydrodynamics, stellar evolution, as well as knowledge of many aspects of computational physics. IAU Symposium 246 brought together experts in all these areas to cover the broad field of dense stellar systems with particular emphasis on the interplay between them and on the comparison between observations and simulations. This volume provides a complete review of the most recent studies in this topical research.
Download or read book Introduction to Galaxy Formation and Evolution written by Andrea Cimatti and published by Cambridge University Press. This book was released on 2019-10-17 with total page 587 pages. Available in PDF, EPUB and Kindle. Book excerpt: Present-day elliptical, spiral and irregular galaxies are large systems made of stars, gas and dark matter. Their properties result from a variety of physical processes that have occurred during the nearly fourteen billion years since the Big Bang. This comprehensive textbook, which bridges the gap between introductory and specialized texts, explains the key physical processes of galaxy formation, from the cosmological recombination of primordial gas to the evolution of the different galaxies that we observe in the Universe today. In a logical sequence, the book introduces cosmology, illustrates the properties of galaxies in the present-day Universe, then explains the physical processes behind galaxy formation in the cosmological context, taking into account the most recent developments in this field. The text ends on how to find distant galaxies with multi-wavelength observations, and how to extract the physical and evolutionary properties based on imaging and spectroscopic data.
Download or read book The Evolution of Galaxies and Their Environment written by David J. Hollenbach and published by . This book was released on 1993 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt:
