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This book introduces the basic concepts of fuzzy collaborative
forecasting and clustering, including its methodology, system
architecture, and applications. It demonstrates how dealing with
disparate data sources is becoming more and more popular due to the
increasing spread of internet applications. The book proposes the
concepts of collaborative computing intelligence and collaborative
fuzzy modeling, and establishes several so-called fuzzy
collaborative systems. It shows how technical constraints, security
issues, and privacy considerations often limit access to some
sources. This book is a valuable source of information for
postgraduates, researchers and fuzzy control system developers, as
it presents a very effective fuzzy approach that can deal with
disparate data sources, big data, and multiple expert decision
making.
Recently many researchers are working on cluster analysis as a main
tool for exploratory data analysis and data mining. A notable
feature is that specialists in di?erent ?elds of sciences are
considering the tool of data clustering to be useful. A major
reason is that clustering algorithms and software are ?exible in
thesensethatdi?erentmathematicalframeworksareemployedinthealgorithms
and a user can select a suitable method according to his
application. Moreover
clusteringalgorithmshavedi?erentoutputsrangingfromtheolddendrogramsof
agglomerativeclustering to more recent self-organizingmaps. Thus, a
researcher or user can choose an appropriate output suited to his
purpose, which is another ?exibility of the methods of clustering.
An old and still most popular method is the K-means which use K
cluster centers. A group of data is gathered around a cluster
center and thus forms a cluster. The main subject of this book is
the fuzzy c-means proposed by Dunn and Bezdek and their variations
including recent studies. A main reasonwhy we concentrate on fuzzy
c-means is that most methodology and application studies infuzzy
clusteringusefuzzy c-means, andfuzzy c-meansshouldbe consideredto
beamajortechniqueofclusteringingeneral,
regardlesswhetheroneisinterested in fuzzy methods or not. Moreover
recent advances in clustering techniques are rapid and we requirea
new textbook that includes recent algorithms.We should also note
that several books have recently been published but the contents do
not include some methods studied here
Recently many researchers are working on cluster analysis as a main
tool for exploratory data analysis and data mining. A notable
feature is that specialists in di?erent ?elds of sciences are
considering the tool of data clustering to be useful. A major
reason is that clustering algorithms and software are ?exible in
thesensethatdi?erentmathematicalframeworksareemployedinthealgorithms
and a user can select a suitable method according to his
application. Moreover
clusteringalgorithmshavedi?erentoutputsrangingfromtheolddendrogramsof
agglomerativeclustering to more recent self-organizingmaps. Thus, a
researcher or user can choose an appropriate output suited to his
purpose, which is another ?exibility of the methods of clustering.
An old and still most popular method is the K-means which use K
cluster centers. A group of data is gathered around a cluster
center and thus forms a cluster. The main subject of this book is
the fuzzy c-means proposed by Dunn and Bezdek and their variations
including recent studies. A main reasonwhy we concentrate on fuzzy
c-means is that most methodology and application studies infuzzy
clusteringusefuzzy c-means, andfuzzy c-meansshouldbe consideredto
beamajortechniqueofclusteringingeneral,
regardlesswhetheroneisinterested in fuzzy methods or not. Moreover
recent advances in clustering techniques are rapid and we requirea
new textbook that includes recent algorithms.We should also note
that several books have recently been published but the contents do
not include some methods studied here
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Integrated Uncertainty in Knowledge Modelling and Decision Making - 9th International Symposium, IUKM 2022, Ishikawa, Japan, March 18-19, 2022, Proceedings (Paperback, 1st ed. 2022)
Katsuhiro Honda, Tomoe Entani, Seiki Ubukata, Van-Nam Huynh, Masahiro Inuiguchi
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R1,972
Discovery Miles 19 720
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 98th
International Symposium on Integrated Uncertainty in Knowledge
Modelling and Decision Making, IUKM 2021, held in Ishikawa, Japan,
in March 2022. The 30 full papers presented were carefully reviewed
and selected from 46 submissions. The papers deal with all aspects
of uncertainty modelling and management and are organized in
topical sections on uncertainty management and decision making,
optimization and statistical methods, pattern classification and
data analysis, machine learning, and economic applications.
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