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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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