|
Showing 1 - 8 of
8 matches in All Departments
Data clustering is a highly interdisciplinary field, the goal of
which is to divide a set of objects into homogeneous groups such
that objects in the same group are similar and objects in different
groups are quite distinct. Thousands of theoretical papers and a
number of books on data clustering have been published over the
past 50 years. However, few books exist to teach people how to
implement data clustering algorithms. This book was written for
anyone who wants to implement or improve their data clustering
algorithms. Using object-oriented design and programming
techniques, Data Clustering in C++ exploits the commonalities of
all data clustering algorithms to create a flexible set of reusable
classes that simplifies the implementation of any data clustering
algorithm. Readers can follow the development of the base data
clustering classes and several popular data clustering algorithms.
Additional topics such as data pre-processing, data visualization,
cluster visualization, and cluster interpretation are briefly
covered. This book is divided into three parts-- * Data Clustering
and C++ Preliminaries: A review of basic concepts of data
clustering, the unified modeling language, object-oriented
programming in C++, and design patterns * A C++ Data Clustering
Framework: The development of data clustering base classes * Data
Clustering Algorithms: The implementation of several popular data
clustering algorithms A key to learning a clustering algorithm is
to implement and experiment the clustering algorithm. Complete
listings of classes, examples, unit test cases, and GNU
configuration files are included in the appendices of this book as
well as in the CD-ROM of the book. The only requirements to compile
the code are a modern C++ compiler and the Boost C++ libraries.
This book is devoted to the mathematical methods of metamodeling
that can be used to speed up the valuation of large portfolios of
variable annuities. It is suitable for advanced undergraduate
students, graduate students, and practitioners. It is the goal of
this book to describe the computational problems and present the
metamodeling approaches in a way that can be accessible to advanced
undergraduate students and practitioners. To that end, the book
will not only describe the theory of these mathematical approaches,
but also present the implementations.
Excel Visual Basic for Applications (VBA) can be used to automate
operations in Excel and is one of the most frequently used software
programs for manipulating data and building models in banks and
insurance companies. An Introduction to Excel VBA Programming: with
Applications in Finance and Insurance introduces readers to the
basic fundamentals of VBA Programming while demonstrating
applications of VBA to solve real-world problems in finance and
insurance. Assuming no prior programming experience and with
reproducible examples using code and data, this text is suitable
for advanced undergraduate students, graduate students, actuaries,
and financial analysts who wish to learn VBA. Features: Presents
the theory behind the algorithms in detail Includes more than 100
exercises with selected solutions Provides VBA code in Excel files
and data to reproduce the results in the book Offers a solutions
manual for qualified instructors
Data clustering is a highly interdisciplinary field, the goal of
which is to divide a set of objects into homogeneous groups such
that objects in the same group are similar and objects in different
groups are quite distinct. Thousands of theoretical papers and a
number of books on data clustering have been published over the
past 50 years. However, few books exist to teach people how to
implement data clustering algorithms. This book was written for
anyone who wants to implement or improve their data clustering
algorithms. Using object-oriented design and programming
techniques, Data Clustering in C++ exploits the commonalities of
all data clustering algorithms to create a flexible set of reusable
classes that simplifies the implementation of any data clustering
algorithm. Readers can follow the development of the base data
clustering classes and several popular data clustering algorithms.
Additional topics such as data pre-processing, data visualization,
cluster visualization, and cluster interpretation are briefly
covered. This book is divided into three parts-- Data Clustering
and C++ Preliminaries: A review of basic concepts of data
clustering, the unified modeling language, object-oriented
programming in C++, and design patterns A C++ Data Clustering
Framework: The development of data clustering base classes Data
Clustering Algorithms: The implementation of several popular data
clustering algorithms A key to learning a clustering algorithm is
to implement and experiment the clustering algorithm. Complete
listings of classes, examples, unit test cases, and GNU
configuration files are included in the appendices of this book as
well as in the downloadable resources. The only requirements to
compile the code are a modern C++ compiler and the Boost C++
libraries.
Excel Visual Basic for Applications (VBA) can be used to automate
operations in Excel and is one of the most frequently used software
programs for manipulating data and building models in banks and
insurance companies. An Introduction to Excel VBA Programming: with
Applications in Finance and Insurance introduces readers to the
basic fundamentals of VBA Programming while demonstrating
applications of VBA to solve real-world problems in finance and
insurance. Assuming no prior programming experience and with
reproducible examples using code and data, this text is suitable
for advanced undergraduate students, graduate students, actuaries,
and financial analysts who wish to learn VBA. Features: Presents
the theory behind the algorithms in detail Includes more than 100
exercises with selected solutions Provides VBA code in Excel files
and data to reproduce the results in the book Offers a solutions
manual for qualified instructors
This book is devoted to the mathematical methods of metamodeling
that can be used to speed up the valuation of large portfolios of
variable annuities. It is suitable for advanced undergraduate
students, graduate students, and practitioners. It is the goal of
this book to describe the computational problems and present the
metamodeling approaches in a way that can be accessible to advanced
undergraduate students and practitioners. To that end, the book
will not only describe the theory of these mathematical approaches,
but also present the implementations.
|
Advanced Data Mining and Applications - 14th International Conference, ADMA 2018, Nanjing, China, November 16-18, 2018, Proceedings (Paperback, 1st ed. 2018)
Guojun Gan, Bohan Li, Xue Li, Shuliang Wang
|
R1,637
Discovery Miles 16 370
|
Ships in 10 - 15 working days
|
This book constitutes the refereed proceedings of the 14th
International Conference on Advanced Data Mining and Applications,
ADMA 2018, held in Nanjing, China in November 2018. The 23 full and
22 short papers presented in this volume were carefully reviewed
and selected from 104 submissions. The papers were organized in
topical sections named: Data Mining Foundations; Big Data; Text and
Multimedia Mining; Miscellaneous Topics.
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. Data Clustering: Theory, Algorithms, and
Applications, Second Edition: covers the basics of data clustering,
includes a list of popular clustering algorithms, and provides
program code that helps users implement clustering algorithms.
|
You may like...
Tenet
John David Washington, Robert Pattinson
Blu-ray disc
(1)
R54
Discovery Miles 540
|