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  • Data Clustering : Algorithms and Applications
    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.

    Price: 130.00 £ | Shipping*: 0.00 £
  • Combining DBSCAN and Grid Based Clustering For Performance Analysis
    Combining DBSCAN and Grid Based Clustering For Performance Analysis


    Price: 33.12 £ | Shipping*: 0.00 £
  • Unsupervised Machine Learning for Clustering in Political and Social Research
    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.

    Price: 17.00 £ | Shipping*: 3.99 £
  • The Golden Palominos Clustering Train 1985 USA 12" vinyl CEL187
    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.

  • 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.

  • How can one organize and categorize images on the PC?

    One way to organize and categorize images on a PC is by creating folders and subfolders based on different criteria such as date, event, or subject. This allows for easy navigation and retrieval of specific images. Another method is to use image management software that allows for tagging and keywording of images, making it easier to search and filter based on specific criteria. Additionally, creating a consistent file naming system can also help in organizing and categorizing images on a PC.

  • Is there a way to better organize and categorize one's thoughts?

    One way to better organize and categorize one's thoughts is through the use of mind mapping techniques. Mind mapping involves creating a visual representation of your thoughts and ideas, allowing you to see connections and relationships between different concepts. Another method is to use a journal or notebook to write down your thoughts and ideas, categorizing them into different sections or topics. Additionally, practicing mindfulness and meditation can help clear your mind and improve focus, making it easier to organize your thoughts effectively.

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  • Model-Based Clustering and Classification for Data Science : With Applications in R
    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.

    Price: 69.99 £ | Shipping*: 0.00 £
  • An Introduction to Spatial Data Science with GeoDa : Volume 2: Clustering Spatial Data
    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.

    Price: 76.99 £ | Shipping*: 0.00 £
  • Self-Adhesive Label Adhesive Sticker Waterproof Durable Classify Blank Tag Sticker Seasoning Jar Label Sticker A6
    Self-Adhesive Label Adhesive Sticker Waterproof Durable Classify Blank Tag Sticker Seasoning Jar Label Sticker A6

    Features: 1. Easy to use: The self-adhesive label sticker can be easily torn off and stuck on any clean surface without using any additional glue or tape. They are suitable for a variety of environments, including homes, offices, shops, and other workplaces. 2. Customizable: These stickers can be customized according to your needs, and the company's logo, name, address or other information can be printed on the stickers. This personalized design makes these label stickers a great tool to promote your product or service. 3. Durable and durable: These self-adhesive label stickers are made of high-quality materials and can maintain good performance even in high temperature, humid or rough environments. The color of the sticker is outstanding and colorful, not easy to fade. 4. Easy to store: Because the self-adhesive label sticker is small in size and light in weight, it will not take up a lot of storage space after use. It is very convenient to arrange on the workbench, drawer or other places, and can be used anytime, anywhere. 5. Reasonable Price: Compared with other regular of label stickers, the price of self-adhesive label stickers is more reasonable and cost-effective. In addition, due to its long service life and the ability to effectively improve the display effect of the product, it is a very cost-effective investment for users. Specification: 100% brand new quality. Material:kraft paper Color: As the picture shows Size:As the picture show(1inch = 2.54cm) Package includes: 10Patches Label Adhesive Sticker Or 1 Roll Label Adhesive Sticker Note: 1.Due to the different monitor and light effect, the actual color of the item might be slightly different from the color which is showed on the pictures. 2.Please forgive 1 cm/0.39 -3 cm/1.18 measuring deviation due to manual measurement. The merchant warrants that their products comply with all applicable laws, and are offered only if they comply with Joom's policies and EU Product Safety and Compliance laws.

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  • MOHAMM-Clear Adhesive Shelf Tag Pockets, Label Holders for Organizing Classify Items, Stationery,
    MOHAMM-Clear Adhesive Shelf Tag Pockets, Label Holders for Organizing Classify Items, Stationery,

    MOHAMM-Clear Adhesive Shelf Tag Pockets, Label Holders for Organizing Classify Items, Stationery,

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  • How do you sort and organize your year 2021?

    I sort and organize my year 2021 by using a combination of digital tools and physical planners. I use a digital calendar to keep track of important dates, appointments, and deadlines, while also utilizing task management apps to prioritize and track my daily to-do lists. Additionally, I use a physical planner to jot down notes, goals, and reflections, allowing me to have a tangible and visual representation of my plans and progress throughout the year. This combination of digital and physical organization helps me stay on top of my commitments and goals for the year.

  • How can one build a fashion label and organize production abroad?

    To build a fashion label and organize production abroad, one should start by conducting thorough research on potential manufacturing partners in the desired country. It's important to visit the facilities, meet with the team, and ensure they have the capabilities to meet your production needs. Establishing clear communication channels and setting up regular visits to oversee production are crucial for maintaining quality control. Additionally, understanding the local regulations, labor laws, and cultural nuances of the country will help in building a successful and sustainable production operation abroad.

  • How can I return orders without a label/price tag at Peek & Cloppenburg?

    If you need to return an order to Peek & Cloppenburg without a label or price tag, you can contact their customer service for assistance. They may provide you with alternative options for returning the item, such as using your order confirmation email or packing slip as proof of purchase. It's important to explain your situation clearly and provide any relevant information to facilitate the return process.

  • When should one categorize?

    One should categorize when there is a need to organize information or objects into distinct groups based on common characteristics or attributes. Categorization helps in making sense of complex information, simplifying decision-making, and improving efficiency in various tasks. It is particularly useful when dealing with large amounts of data or when trying to identify patterns and relationships among different items. Overall, categorization can help in creating order and structure, making it easier to understand and work with the information or objects at hand.

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