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The purpose of this book is to thoroughly prepare the reader for applied research in clustering. Cluster analysis comprises a class of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used in several domains of research, such as social sciences, psychology, and marketing, highlighting its multidisciplinary nature. This book provides an accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen real-life datasets and extensive data analyses. The procedures addressed in this book include traditional hard clustering methods and up-to-date developments in soft clustering. Attention is paid to practical examples and applications through the open source statistical software R. Commented R code and output for conducting, step by step, complete cluster analyses are available. The book is intended for researchers interested in applying clustering methods. Basic notions on theoretical issues and on R are provided so that professionals as well as novices with little or no background in the subject will benefit from the book.
This publication presents an overview of a series of "Hard Talks" held in Azerbaijan, Bosnia and Herzegovina, Georgia, Kazakhstan, Serbia and Ukraine in 2016-2019, organized by UNECE. They promoted demand-driven debates held in each country on what is needed to change for the private sector to invest in sustainable energy and substantially increase the uptake of renewable energy. Information and data included in this report are extracted from the activities, which aimed to investigate barriers that hinder the full unfolding of renewable energy potential, and facilitated an open exchange between political decision makers, governmental officials, project developers, investors and technology providers understand the objectives and actions of other countries. The project aims to contribute concretely to climate change mitigation and sustainable development
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