Notably, real problems are increasingly complex and require sophisticated models and
algorithms capable of quickly dealing with large data sets and finding optimal solutions …

Cloud computing has rapidly emerged as a model for delivering Internet-based utility
computing services. Infrastructure as a Service (IaaS) is one of the most important and …

Machine learning develops rapidly, which has made many theoretical breakthroughs and is
widely applied in various fields. Optimization, as an important part of machine learning, has …

Clustering non-Euclidean data is difficult, and one of the most used algorithms besides
hierarchical clustering is the popular algorithm Partitioning Around Medoids (PAM), also …

Data analysis plays an indispensable role for understanding various phenomena. Cluster
analysis, primitive exploration with little or no prior knowledge, consists of research …

Decision trees have become one of the most powerful and popular approaches in
knowledge discovery and data mining; it is the science of exploring large and complex …

This chapter presents a tutorial overview of the main clustering methods used in Data
Mining. The goal is to provide a self-contained review of the concepts and the mathematics …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …

We argue that there are many clustering algorithms, because the notion of" cluster" cannot
be precisely defined. Clustering is in the eye of the beholder, and as such, researchers have …

The density-based spatial clustering of applications with noise (DBSCAN) is regarded as a
pioneering algorithm of the density-based clustering technique. It provides the ability to …