Products related to Clustering:
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Unsupervised Machine Learning for Clustering in Political and Social Research
In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill.Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program.This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data.A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts.Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering.
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Data Clustering : Algorithms and Applications
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 validationIn 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.
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Combining DBSCAN and Grid Based Clustering For Performance Analysis
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The Golden Palominos Clustering Train 1985 USA 12" vinyl CEL187
GOLDEN PALOMINOS Clustering Train (Rare 1985 US 4-track promo only 12 featuring 4:10 Edited Version & 6:04 Long Version both with vocals by Michael Stipe b/w Kind Of True & Silver Bullet housed in custom stickered die-cut sleeve CEL187)
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Where is the k-means clustering used?
K-means clustering is used in various fields such as machine learning, data mining, pattern recognition, and image analysis. It is commonly used in market segmentation, customer profiling, document clustering, and image compression. Additionally, k-means clustering is also used in biological data analysis to group genes with similar expression patterns and in social network analysis to identify communities of users with similar interests or behaviors.
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Which topics would you most likely use in a small presentation about k-means clustering?
In a small presentation about k-means clustering, I would likely cover the following topics: 1. Introduction to clustering and the concept of unsupervised learning. 2. Explanation of the k-means algorithm, including how it works and its key components such as centroids and clusters. 3. Steps involved in implementing k-means clustering, such as selecting the number of clusters (k) and evaluating the clustering results.
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How does growth progress?
Growth progresses through a series of stages, starting from infancy and continuing through childhood, adolescence, and adulthood. During these stages, individuals experience physical, cognitive, emotional, and social development. Factors such as genetics, environment, nutrition, and experiences all play a role in shaping an individual's growth trajectory. As individuals grow, they acquire new skills, knowledge, and perspectives that contribute to their overall development and well-being.
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How does language development progress?
Language development progresses in stages, starting with cooing and babbling in infancy, then moving on to single words, two-word phrases, and eventually more complex sentences. Children learn language through exposure to spoken language, imitation, and reinforcement from caregivers. As they grow, they continue to expand their vocabulary, grammar, and communication skills through interactions with others and practice in various social contexts. Language development is a gradual process that is influenced by both biological factors and environmental experiences.
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Model-Based Clustering and Classification for Data Science : With Applications in R
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.
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An Introduction to Spatial Data Science with GeoDa : Volume 2: Clustering Spatial Data
This book is the second in a two-volume series that introduces the field of spatial data science.It moves beyond pure data exploration to the organization of observations into meaningful groups, i.e., spatial clustering.This constitutes an important component of so-called unsupervised learning, a major aspect of modern machine learning. The distinctive aspects of the book are both to explore ways to spatialize classic clustering methods through linked maps and graphs, as well as the explicit introduction of spatial contiguity constraints into clustering algorithms.Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques and their relative advantages and disadvantages.The book also constitutes the definitive user’s guide for these methods as implemented in the GeoDa open source software for spatial analysis. It is organized into three major parts, dealing with dimension reduction (principal components, multidimensional scaling, stochastic network embedding), classic clustering methods (hierarchical clustering, k-means, k-medians, k-medoids and spectral clustering), and spatially constrained clustering methods (both hierarchical and partitioning).It closes with an assessment of spatial and non-spatial cluster properties. The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns as expressed in spatial clusters of observations.Familiarity with the material in Volume 1 is assumed, especially the analysis of local spatial autocorrelation and the full range of visualization methods.
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Teachers Learning : Professional Development and Education
A collaborative series with the University of Cambridge Faculty of Education highlighting leading-edge research across Teacher Education, International Education Reform and Language Education. Teachers Learning focuses on continuing teacher's professional development through accounts of recent enquiries.From presenting evidence and arguments about the teacher's role and profession, knowledge and knowledge creation, other organisations in the educational landscape, issues of power, status and development, this book highlights the crucial importance and effectiveness of teachers' learning benefitting pupils and classroom learning.
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Learning Curve : Lessons on Leadership, Education, and Personal Growth
Personal development is not easy. So why do so many leadership and self-help books read like an author's highlight reel?Learning Curve pushes back against conventional literature by discussing the real, behind-the-scenes challenges of a developing school leader. Blending authentic anecdotes with relevant research, Learning Curve transforms complicated life principles into useful pearls of wisdom.Whether you are looking for a few helpful ideas-or are searching for a complete mental makeover-Learning Curve serves as a practical resource for motivated, everyday school leaders.
Price: 18.99 £ | Shipping*: 3.99 £
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How does human development progress?
Human development progresses through a series of stages that are influenced by both biological and environmental factors. These stages include physical, cognitive, emotional, and social development. As individuals grow and mature, they acquire new skills, knowledge, and experiences that shape their understanding of the world and their place in it. Development is a lifelong process that continues from infancy through adulthood and is influenced by a combination of genetics, environment, and personal experiences.
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How does sensory development progress?
Sensory development progresses through a series of stages, beginning with basic sensory experiences such as touch, taste, and smell in infancy. As children grow, they develop more refined sensory abilities, such as improved vision and hearing, and begin to integrate sensory information to make sense of their environment. This process continues into adolescence and adulthood, with individuals becoming more adept at interpreting and responding to sensory stimuli. Overall, sensory development progresses from basic sensory experiences to more complex and integrated sensory abilities as individuals mature.
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How does the growth progress?
The growth progress is a gradual and continuous process that involves an increase in size, development, and maturity. It can be observed in various aspects of life, such as physical, emotional, intellectual, and professional growth. This progress is often influenced by factors such as genetics, environment, nutrition, and experiences. As individuals age, they go through different stages of growth, each with its own set of milestones and challenges. Overall, growth progress is a dynamic and ongoing journey that shapes individuals into who they are.
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How does beard growth progress?
Beard growth progresses in stages, starting with the initial growth phase where hair follicles become active and start producing hair. This is followed by the transitional phase where the hair becomes thicker and more visible. Finally, the mature phase is reached where the beard reaches its full potential in terms of length and thickness. The rate of growth and the overall appearance of the beard can vary depending on genetics, age, and hormonal factors.
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