Found inside – Page iiThis is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. SNA techniques are derived from sociological and social-psychological theories and take into account the whole network (or, in case of very large networks such as Twitter -- a large segment of the network). The Encyclopedia of Data Warehousing and Mining, Second Edition, offers thorough exposure to the issues of importance in the rapidly changing field of data warehousing and mining. Found inside – Page 101There are many metrics to choose from to compare phenotypic profiles, the most commonly used ... most studies use agglomerative hierarchical clustering. Found insideThe book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This book provides an introduction to the field of Network Science and provides the groundwork for a computational, algorithm-based approach to network and system analysis in a new and important way. Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by techniques chosen almost through trial-and-error. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures. Found insideAuthor Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. JMP 11 Multivariate Methods shows you how to take advantage of the modeling platforms Multivariate, Cluster, Discriminant, Principal Components, and Partial Least Squares. 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. The book describes the theoretical choices a market researcher has to make with regard to each technique, discusses how these are converted into actions in IBM SPSS version 22 and how to interpret the output. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management. This book has fundamental theoretical and practical aspects of data analysis, useful for beginners and experienced researchers that are looking for a recipe or an analysis approach. Found insideThis book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R. The visualization is based on the factoextra R ... Found inside – Page iiWhile intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice. Measuring the User Experience was the first book that focused on how to quantify the user experience. "This book addresses existing solutions for data mining, with particular emphasis on potential real-world applications. Found insideStatistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underl Found insideThis foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. Since the initial work on constrained clustering, there have been numerous advances in methods, applications, and our understanding of the theoretical properties of constraints and constrained clustering algorithms. This book serves as a basic guide for a wide range of audiences from less familiar with metabolomics techniques to more experienced researchers seeking to understand complex biological systems from the systems biology approach. Most of the entries in this preeminent work include useful literature references. Although there are several good books on unsupervised machine learning, we felt that many of them are too theoretical. This book provides practical guide to cluster analysis, elegant visualization and interpretation. It contains 5 parts. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China. Found inside – Page iThis first part closes with the MapReduce (MR) model of computation well-suited to processing big data using the MPI framework. In the second part, the book focuses on high-performance data analytics. This is the first book to take a truly comprehensive look at clustering. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. Found insideStarting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business ... Found insideThis book is published open access under a CC BY 4.0 license. Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets. This book focuses on partitional clustering algorithms, which are commonly used in engineering and computer scientific applications. The goal of this volume is to summarize the state-of-the-art in partitional clustering. Found insideThis is an introductory textbook on spatial analysis and spatial statistics through GIS. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. Found insideThe work addresses problems from gene regulation, neuroscience, phylogenetics, molecular networks, assembly and folding of biomolecular structures, and the use of clustering methods in biology. Found insideThe optimization methods considered are proved to be meaningful in the contexts of data analysis and clustering. The material presented in this book is quite interesting and stimulating in paradigms, clustering and optimization. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). Found insideThis book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, ... Found inside – Page 176However, traditional algorithms for constructing such tree structures, whether bottom-up (agglomerative) or top-down (divisive), typically are not able to ... This book is a practical guide not only on how to operate the tool but also on why the tool is designed and what implications of various patterns that require special attention. This book is written with a minimum amount of jargon. Are too theoretical of research hierarchical agglomerative clustering is typically visualized as select one a manageable and concise presentation, practical. Its second edition, this book synthesizes of a broad array of into... 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