Cluster Models And Other Topics

Cluster Models And Other Topics PDF Author: S A Chin
Publisher: World Scientific
ISBN: 9814513628
Category : Science
Languages : en
Pages : 520

Get Book Here

Book Description
This volume consists of contributions from some of Japan's most eminent nuclear theorists. The cluster model of the nucleus is discussed pedagogically and the current status of the field is surveyed. A contribution on Monte Carlo Methods and Lattice Gauge Theories gives nuclear theorists a glimpse of related developments in QCD and Gauge Theories. Few Body Systems are reviewed by Y Akaishi, paying special attention to the ATMS Multiple Scattering Method.

Cluster Models And Other Topics

Cluster Models And Other Topics PDF Author: S A Chin
Publisher: World Scientific
ISBN: 9814513628
Category : Science
Languages : en
Pages : 520

Get Book Here

Book Description
This volume consists of contributions from some of Japan's most eminent nuclear theorists. The cluster model of the nucleus is discussed pedagogically and the current status of the field is surveyed. A contribution on Monte Carlo Methods and Lattice Gauge Theories gives nuclear theorists a glimpse of related developments in QCD and Gauge Theories. Few Body Systems are reviewed by Y Akaishi, paying special attention to the ATMS Multiple Scattering Method.

Model-Based Clustering and Classification for Data Science

Model-Based Clustering and Classification for Data Science PDF Author: Charles Bouveyron
Publisher: Cambridge University Press
ISBN: 1108640591
Category : Mathematics
Languages : en
Pages : 447

Get Book Here

Book Description
Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.

Clustering Algorithms

Clustering Algorithms PDF Author: John A. Hartigan
Publisher: John Wiley & Sons
ISBN:
Category : Mathematics
Languages : en
Pages : 374

Get Book Here

Book Description
Shows how Galileo, Newton, and Einstein tried to explain gravity. Discusses the concept of microgravity and NASA's research on gravity and microgravity.

Cluster Models and Other Topics

Cluster Models and Other Topics PDF Author: Yoshinori Akaishi
Publisher: World Scientific
ISBN: 9789971500788
Category : Science
Languages : en
Pages : 538

Get Book Here

Book Description
This volume consists of contributions from some of Japan's most eminent nuclear theorists. The cluster model of the nucleus is discussed pedagogically and the current status of the field is surveyed. A contribution on Monte Carlo Methods and Lattice Gauge Theories gives nuclear theorists a glimpse of related developments in QCD and Gauge Theories. Few Body Systems are reviewed by Y Akaishi, paying special attention to the ATMS Multiple Scattering Method.

Model-Based Clustering and Classification for Data Science

Model-Based Clustering and Classification for Data Science PDF Author: Charles Bouveyron
Publisher: Cambridge University Press
ISBN: 110849420X
Category : Business & Economics
Languages : en
Pages : 446

Get Book Here

Book Description
Colorful example-rich introduction to the state-of-the-art for students in data science, as well as researchers and practitioners.

Text Mining with R

Text Mining with R PDF Author: Julia Silge
Publisher: "O'Reilly Media, Inc."
ISBN: 1491981628
Category : Computers
Languages : en
Pages : 193

Get Book Here

Book Description
Chapter 7. Case Study : Comparing Twitter Archives; Getting the Data and Distribution of Tweets; Word Frequencies; Comparing Word Usage; Changes in Word Use; Favorites and Retweets; Summary; Chapter 8. Case Study : Mining NASA Metadata; How Data Is Organized at NASA; Wrangling and Tidying the Data; Some Initial Simple Exploration; Word Co-ocurrences and Correlations; Networks of Description and Title Words; Networks of Keywords; Calculating tf-idf for the Description Fields; What Is tf-idf for the Description Field Words?; Connecting Description Fields to Keywords; Topic Modeling.

Clusters, Orders, and Trees: Methods and Applications

Clusters, Orders, and Trees: Methods and Applications PDF Author: Fuad Aleskerov
Publisher: Springer
ISBN: 1493907425
Category : Mathematics
Languages : en
Pages : 404

Get Book Here

Book Description
The volume is dedicated to Boris Mirkin on the occasion of his 70th birthday. In addition to his startling PhD results in abstract automata theory, Mirkin’s ground breaking contributions in various fields of decision making and data analysis have marked the fourth quarter of the 20th century and beyond. Mirkin has done pioneering work in group choice, clustering, data mining and knowledge discovery aimed at finding and describing non-trivial or hidden structures—first of all, clusters, orderings and hierarchies—in multivariate and/or network data. This volume contains a collection of papers reflecting recent developments rooted in Mirkin’s fundamental contribution to the state-of-the-art in group choice, ordering, clustering, data mining and knowledge discovery. Researchers, students and software engineers will benefit from new knowledge discovery techniques and application directions.

Handbook of Cluster Analysis

Handbook of Cluster Analysis PDF Author: Christian Hennig
Publisher: CRC Press
ISBN: 1466551895
Category : Business & Economics
Languages : en
Pages : 753

Get Book Here

Book Description
Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools.The

The Random-Cluster Model

The Random-Cluster Model PDF Author: Geoffrey R. Grimmett
Publisher: Springer Science & Business Media
ISBN: 3540328912
Category : Mathematics
Languages : en
Pages : 392

Get Book Here

Book Description
The random-cluster model has emerged as a key tool in the mathematical study of ferromagnetism. It may be viewed as an extension of percolation to include Ising and Potts models, and its analysis is a mix of arguments from probability and geometry. The Random-Cluster Model contains accounts of the subcritical and supercritical phases, together with clear statements of important open problems. The book includes treatment of the first-order (discontinuous) phase transition.

Practical Guide to Cluster Analysis in R

Practical Guide to Cluster Analysis in R PDF Author: Alboukadel Kassambara
Publisher: STHDA
ISBN: 1542462703
Category : Education
Languages : en
Pages : 168

Get Book Here

Book Description
Although there are several good books on unsupervised machine learning, we felt that many of them are too theoretical. This book provides practical guide to cluster analysis, elegant visualization and interpretation. It contains 5 parts. Part I provides a quick introduction to R and presents required R packages, as well as, data formats and dissimilarity measures for cluster analysis and visualization. Part II covers partitioning clustering methods, which subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst. Partitioning clustering approaches include: K-means, K-Medoids (PAM) and CLARA algorithms. In Part III, we consider hierarchical clustering method, which is an alternative approach to partitioning clustering. The result of hierarchical clustering is a tree-based representation of the objects called dendrogram. In this part, we describe how to compute, visualize, interpret and compare dendrograms. Part IV describes clustering validation and evaluation strategies, which consists of measuring the goodness of clustering results. Among the chapters covered here, there are: Assessing clustering tendency, Determining the optimal number of clusters, Cluster validation statistics, Choosing the best clustering algorithms and Computing p-value for hierarchical clustering. Part V presents advanced clustering methods, including: Hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and Density-based clustering.