Abstract Semi-supervised clustering (SSC), a technique integrating semi-supervised
learning and clustering analysis, incorporates the given prior information (eg, class labels …

Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that
feature ubiquitously in modern data science, and play a key role in many learning-based …

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 …

Clustering is traditionally viewed as an unsupervised method for data analysis. However, in
some cases information about the problem domain is available in addition to the data …

Many algorithms rely critically on being given a good metric over their inputs. For instance,
data can often be clustered in many “plausible” ways, and if a clustering algorithm such as K …

Liu has written a comprehensive text on Web mining, which consists of two parts. The first
part covers the data mining and machine learning foundations, where all the essential …

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …

Resource management in data centres continues to be a critical problem due to increased
infrastructure complexity and dynamic workload conditions. Workload and energy …

Spectral clustering and path-based clustering are two recently developed clustering
approaches that have delivered impressive results in a number of challenging clustering …

In recent years, multi-view clustering algorithms have shown promising performance by
combining multiple sources or views of datasets. A problem that has not been addressed …