Finding Groups In Data
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| Author | : Leonard Kaufman |
| Publisher | : John Wiley & Sons |
| Total Pages | : 368 |
| Release | : 2009-09-25 |
| Genre | : Mathematics |
| ISBN | : 0470317485 |
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "Cluster analysis is the increasingly important and practical subject of finding groupings in data. The authors set out to write a book for the user who does not necessarily have an extensive background in mathematics. They succeed very well." —Mathematical Reviews "Finding Groups in Data [is] a clear, readable, and interesting presentation of a small number of clustering methods. In addition, the book introduced some interesting innovations of applied value to clustering literature." —Journal of Classification "This is a very good, easy-to-read, and practical book. It has many nice features and is highly recommended for students and practitioners in various fields of study." —Technometrics An introduction to the practical application of cluster analysis, this text presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. This book discusses various types of data, including interval-scaled and binary variables as well as similarity data, and explains how these can be transformed prior to clustering.
| Author | : Leonard Kaufman |
| Publisher | : Wiley-Interscience |
| Total Pages | : 376 |
| Release | : 1990-03-22 |
| Genre | : Mathematics |
| ISBN | : |
Partitioning around medoids (Program PAM). Clustering large applications (Program CLARA). Fuzzy analysis (Program FANNY). Agglomerative Nesting (Program AGNES). Divisive analysis (Program DIANA). Monothetic analysis (Program MONA). Appendix.
| Author | : Shen Liu |
| Publisher | : Academic Press |
| Total Pages | : 208 |
| Release | : 2015-11-20 |
| Genre | : Mathematics |
| ISBN | : 0081006519 |
Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. - Advanced computational and statistical methodologies for analysing big data are developed - Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable - Case studies are discussed to demonstrate the implementation of the developed methods - Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation - Computing code/programs are provided where appropriate
| Author | : Guojun Gan |
| Publisher | : SIAM |
| Total Pages | : 430 |
| Release | : 2020-11-10 |
| Genre | : Mathematics |
| ISBN | : 1611976332 |
Data clustering, also known as cluster analysis, is an unsupervised process that divides a set of objects into homogeneous groups. Since the publication of the first edition of this monograph in 2007, development in the area has exploded, especially in clustering algorithms for big data and open-source software for cluster analysis. This second edition reflects these new developments, covers the basics of data clustering, includes a list of popular clustering algorithms, and provides program code that helps users implement clustering algorithms. Data Clustering: Theory, Algorithms and Applications, Second Edition will be of interest to researchers, practitioners, and data scientists as well as undergraduate and graduate students.
| Author | : Nigro, Hector Oscar |
| Publisher | : IGI Global |
| Total Pages | : 312 |
| Release | : 2007-07-31 |
| Genre | : Computers |
| ISBN | : 1599046202 |
"Prior knowledge in data mining is helpful for selecting suitable data and mining techniques, pruning the space of hypothesis, representing the output in a comprehensible way, and improving the overall method. This book examines methodologies and research for the development of ontological foundations for data mining to enhance the ability of ontology utilization and design"--Provided by publisher.
| Author | : Cole Nussbaumer Knaflic |
| Publisher | : John Wiley & Sons |
| Total Pages | : 284 |
| Release | : 2015-10-09 |
| Genre | : Mathematics |
| ISBN | : 1119002265 |
Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it!
| Author | : Linnet Taylor |
| Publisher | : Springer |
| Total Pages | : 249 |
| Release | : 2016-12-28 |
| Genre | : Philosophy |
| ISBN | : 3319466089 |
The goal of the book is to present the latest research on the new challenges of data technologies. It will offer an overview of the social, ethical and legal problems posed by group profiling, big data and predictive analysis and of the different approaches and methods that can be used to address them. In doing so, it will help the reader to gain a better grasp of the ethical and legal conundrums posed by group profiling. The volume first maps the current and emerging uses of new data technologies and clarifies the promises and dangers of group profiling in real life situations. It then balances this with an analysis of how far the current legal paradigm grants group rights to privacy and data protection, and discusses possible routes to addressing these problems. Finally, an afterword gathers the conclusions reached by the different authors and discuss future perspectives on regulating new data technologies.
| Author | : Tiffany Timbers |
| Publisher | : CRC Press |
| Total Pages | : 466 |
| Release | : 2022-07-15 |
| Genre | : Business & Economics |
| ISBN | : 1000579646 |
Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows. Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects. The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course.
| Author | : PY Cheng |
| Publisher | : Xlibris Corporation |
| Total Pages | : 282 |
| Release | : 2014-06-28 |
| Genre | : Computers |
| ISBN | : 1499002831 |
The first characteristic of this book is the skipping of difficult theories, starting directly with typical examples, and the working out of the answers clearly by computer software. The aim is to help readers to be able to solve basic statistical problems in various Common Experimental Designs as soon as possible, and to be confident to run the tests and interpret the computer output, without being hesitated by the starting with large amount of difficult theories behind. This book has been reviewed by some experts as ‘...very practical, illustrative and directive, and is very useful for both practitioners and those who perform statistical analysis in experimental designs...’ On the other hand, there is a general belief that after getting a significant Anova result, using the built-in ‘Analysis TookPak’ Excel Add-In, nothing can be done further to find where do the differences exist, and we must use large packages such as SPSS to complete the job! However, we find that this might not be absolutely true. We can often find similar results as using SPSS by combining the overall Anova results with a few, simple, manual steps introduced in this book!
| Author | : Meredith Ringel Morris |
| Publisher | : Springer Nature |
| Total Pages | : 85 |
| Release | : 2022-06-01 |
| Genre | : Computers |
| ISBN | : 303102270X |
Today, Web search is treated as a solitary experience. Web browsers and search engines are typically designed to support a single user, working alone. However, collaboration on information-seeking tasks is actually commonplace. Students work together to complete homework assignments, friends seek information about joint entertainment opportunities, family members jointly plan vacation travel, and colleagues jointly conduct research for their projects. As improved networking technologies and the rise of social media simplify the process of remote collaboration, and large, novel display form-factors simplify the process of co-located group work, researchers have begun to explore ways to facilitate collaboration on search tasks. This lecture investigates the who, what, where, when and why of collaborative search, and gives insight in how emerging solutions can address collaborators' needs. Table of Contents: Introduction / Who? / What? / Where? / When? / Why? / Conclusion: How?
