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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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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What entertainment media and entertainment electronics are available?
There is a wide range of entertainment media and electronics available, including streaming services like Netflix, Hulu, and Amazon Prime for watching movies and TV shows. Additionally, there are gaming consoles such as PlayStation, Xbox, and Nintendo Switch for playing video games. Other entertainment electronics include smart TVs, sound systems, and virtual reality headsets for an immersive experience. Furthermore, there are also e-readers and audiobook services for those who enjoy reading and listening to books.
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Is Baroque music serious music or entertainment music?
Baroque music can be seen as both serious music and entertainment music. On one hand, it was often composed for religious or ceremonial purposes, and its intricate compositions and use of counterpoint demonstrate a high level of musical sophistication. On the other hand, Baroque music was also performed in social settings and was meant to entertain and delight audiences. Its lively rhythms and expressive melodies were often used for dancing and other forms of entertainment. Therefore, Baroque music can be appreciated for its serious artistic qualities as well as its ability to provide enjoyment and entertainment.
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Streaming Music : Practices, Media, Cultures
Streaming Music examines how the Internet has become integrated in contemporary music use, by focusing on streaming as a practice and a technology for music consumption.The backdrop to this enquiry is the digitization of society and culture, where the music industry has undergone profound disruptions, and where music streaming has altered listening modes and meanings of music in everyday life.The objective of Streaming Music is to shed light on what these transformations mean for listeners, by looking at their adaptation in specific cultural contexts, but also by considering how online music platforms and streaming services guide music listeners in specific ways.Drawing on case studies from Moscow and Stockholm, and providing analysis of Spotify, VK and YouTube as popular but distinct sites for music, Streaming Music discusses, through a qualitative, cross-cultural, study, questions around music and value, music sharing, modes of engaging with music, and the way that contemporary music listening is increasingly part of mobile, automated and computational processes.Offering a nuanced perspective on these issues, it adds to research about music and digital media, shedding new light on music cultures as they appear today.As such, this volume will appeal to scholars of media, sociology and music with interests in digital technologies.
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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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Content Production for Digital Media : An Introduction
This book provides an introduction to digital media content production in the twenty-first century.It explores the kinds of content production that are undertaken in professions that include journalism, public relations and marketing.The book provides an insight into content moderation and addresses the legal and ethical issues that content producers face, as well as how these issues can be effectively managed.Chapters also contain interviews with media professionals, and quizzes that allow readers to consolidate the knowledge they have gathered through their reading of that chapter.
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Is a 16 Mbit line sufficient for online gaming/streaming?
A 16 Mbit line may be sufficient for online gaming and streaming, but it may not provide the best experience, especially if multiple devices are using the internet at the same time. Online gaming and streaming typically require a stable and fast internet connection to minimize lag and buffering. For a smoother experience, a higher bandwidth such as 25-50 Mbit may be more suitable, especially for high-definition streaming and competitive gaming. Additionally, factors such as network congestion and latency can also impact the overall performance of online gaming and streaming.
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Which PC is best suited for gaming, streaming, and video editing?
A high-performance PC with a powerful processor (such as an Intel Core i9 or AMD Ryzen 9), a dedicated graphics card (like an NVIDIA GeForce RTX 3080 or AMD Radeon RX 6800 XT), ample RAM (at least 16GB), and fast storage (SSD) would be best suited for gaming, streaming, and video editing. These components will ensure smooth gameplay, seamless streaming, and fast video rendering capabilities. Additionally, a good cooling system and high-quality monitor are also important for an optimal experience.
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Which PC laptop is good for video editing, gaming, and streaming?
A good PC laptop for video editing, gaming, and streaming would be the ASUS ROG Zephyrus G14. It features a powerful AMD Ryzen 9 processor and NVIDIA GeForce RTX 2060 graphics card, making it capable of handling demanding video editing software and high-end gaming. Additionally, its compact and lightweight design makes it convenient for streaming on the go. The laptop also has a high-quality display and good cooling system, ensuring a smooth and immersive experience for all three activities.
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What would be good PC parts for gaming, streaming, and video editing?
For gaming, streaming, and video editing, it's important to have a powerful CPU such as an Intel Core i7 or AMD Ryzen 7 for multitasking and processing heavy workloads. A high-end graphics card like an NVIDIA GeForce RTX 3070 or AMD Radeon RX 6700 XT would be ideal for smooth gaming and video rendering. Additionally, at least 16GB of RAM (preferably 32GB) and a fast SSD for storage are essential for quick data access and smooth performance. Lastly, a reliable power supply and a good cooling system are important to ensure stability and longevity of the system during intense usage.
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