Products related to 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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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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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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What is a cultural heritage?
Cultural heritage refers to the traditions, customs, beliefs, and artifacts that are passed down from generation to generation within a society. It encompasses the tangible and intangible aspects of a culture, including historical sites, monuments, art, music, language, and rituals. Cultural heritage plays a crucial role in shaping a community's identity and preserving its unique heritage for future generations. It is an important part of a society's history and contributes to its sense of belonging and continuity.
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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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Is the Drachenschanze a German cultural heritage site?
Yes, the Drachenschanze is considered a German cultural heritage site. It is a historic site in the Harz Mountains that has been preserved and protected due to its cultural significance. The Drachenschanze is a medieval fortification that played a role in the region's history, making it an important part of Germany's cultural heritage. Its historical and architectural significance has led to its recognition as a cultural heritage site in Germany.
Similar search terms for Clustering:
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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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Digital Cultural Heritage
This book provides an overview of various application spheres and supports further innovations needed in information management and in the processes of knowledge generation.The professions, organizations and scientific associations involved are unusually challenged by the complexity of the data situation.Cartography has always been the central field of application for georeferencing digital cultural heritage (DCH) objects.It is particularly important in enabling spatial relation analysis between any number of DCH objects or of their granular details.In addition to the pure geometric aspects, the cognitive relations that lead to knowledge representation and derivation of innovative use processes are also of increasing importance.Further, there is a societal demand for spatial reference and analytics (e.g. the extensive use of cognitive concepts of "map" and "atlas" for a variety of social topics in the media).There is a huge geometrical-logical-cognitive potential for complex, multimedia, digital-cultural-heritage databases and stakeholders expect handling, transmission and processing operations with guaranteed long-term availability for all other stakeholders.In the future, whole areas of digital multimedia databases will need to be processed to further our understanding of historical and cultural contexts.This is an important concern for the information society and presents significant challenges for cartography in all these domains. This book collects innovative technical and scientific work on the entire process of object digitization, including detail extraction, archiving and interoperability of multimedia DCH data.
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The Cultural Heritage of Meghalaya
The state of Meghalaya, formed on 21 January 1972, is a state of fascinating socio-cultural significance.Its heritage can be traced from the prehistoric times of Stone Age upto the present.Though comprising mainly of the matrilineal Khasi, Garo, and Jaintia tribes – the state also houses many other lesser known communities such as the Hajong, Sakachep, Biate, Koch, Dalu, Margnar and the Nepali.All these communities find voice in this volume. The Cultural Heritage of Meghalaya looks at the state of Meghalaya exhaustively from the perspective of heritage documentation and maintenance.
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Is Drachenlord and the Drachengame considered German cultural heritage?
Drachenlord and the Drachengame are not officially recognized as German cultural heritage. While they may have gained a following and become a notable part of internet culture in Germany, they do not hold the same status as traditional cultural elements such as literature, music, or art that are typically associated with being part of a country's cultural heritage. Additionally, the controversial nature of Drachenlord and the Drachengame may make it less likely to be officially recognized as cultural heritage.
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Is the Drachenlord and the Drachengame German cultural heritage?
The Drachenlord and the Drachengame are not considered German cultural heritage in the traditional sense. They are more of a modern internet phenomenon that has gained popularity in certain online communities. While they may have a following and be part of contemporary German internet culture, they do not hold the same historical or cultural significance as traditional German heritage sites or practices.
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What does cultural appropriation of Bavarian traditional clothing look like?
Cultural appropriation of Bavarian traditional clothing can look like non-Bavarian individuals wearing traditional Bavarian garments, such as dirndls and lederhosen, without understanding or respecting the cultural significance behind these items. It can also involve using these traditional clothing items in a disrespectful or caricatured manner, such as wearing them as costumes for entertainment purposes without understanding their historical and cultural significance. Additionally, cultural appropriation of Bavarian traditional clothing can occur when these garments are mass-produced and sold without proper acknowledgment or compensation to the Bavarian communities from which they originate.
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What does handcrafted mean?
Handcrafted refers to items that are made by hand, often by skilled artisans or craftspeople. These items are typically created with attention to detail, care, and craftsmanship, resulting in unique and high-quality products. Handcrafted items often showcase the individuality and creativity of the maker, and can be valued for their authenticity and personal touch.
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