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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 book presents an exciting collection of contributions based on
the workshop "Bringing Maths to Life" held October 27-29, 2014 in
Naples, Italy. The state-of-the art research in biology and the
statistical and analytical challenges facing huge masses of data
collection are treated in this Work. Specific topics explored in
depth surround the sessions and special invited sessions of the
workshop and include genetic variability via differential
expression, molecular dynamics and modeling, complex biological
systems viewed from quantitative models, and microscopy images
processing, to name several. In depth discussions of the
mathematical analysis required to extract insights from complex
bodies of biological datasets, to aid development in the field
novel algorithms, methods and software tools for genetic
variability, molecular dynamics, and complex biological systems are
presented in this book. Researchers and graduate students in
biology, life science, and mathematics/statistics will find the
content useful as it addresses existing challenges in identifying
the gaps between mathematical modeling and biological research. The
shared solutions will aid and promote further collaboration between
life sciences and mathematics.
This proceedings volume is a collection of peer reviewed papers
presented at the 8th International Conference on Soft Methods in
Probability and Statistics (SMPS 2016) held in Rome (Italy). The
book is dedicated to Data science which aims at developing
automated methods to analyze massive amounts of data and to extract
knowledge from them. It shows how Data science employs various
programming techniques and methods of data wrangling, data
visualization, machine learning, probability and statistics. The
soft methods proposed in this volume represent a collection of
tools in these fields that can also be useful for data science.
This book presents an exciting collection of contributions based on
the workshop "Bringing Maths to Life" held October 27-29, 2014 in
Naples, Italy. The state-of-the art research in biology and the
statistical and analytical challenges facing huge masses of data
collection are treated in this Work. Specific topics explored in
depth surround the sessions and special invited sessions of the
workshop and include genetic variability via differential
expression, molecular dynamics and modeling, complex biological
systems viewed from quantitative models, and microscopy images
processing, to name several. In depth discussions of the
mathematical analysis required to extract insights from complex
bodies of biological datasets, to aid development in the field
novel algorithms, methods and software tools for genetic
variability, molecular dynamics, and complex biological systems are
presented in this book. Researchers and graduate students in
biology, life science, and mathematics/statistics will find the
content useful as it addresses existing challenges in identifying
the gaps between mathematical modeling and biological research. The
shared solutions will aid and promote further collaboration between
life sciences and mathematics.
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