Clustering algorithms aim at finding dense regions of data based on similarities and
dissimilarities of data points. Noise and outliers contribute to the computational procedure of …

In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the
shortage of data monitoring devices and the difficulty of comprehensive coverage of …

Big data has increasingly become predominant in many research fields affecting human
knowledge, including medicine and engineering. Cluster analysis, or clustering, is widely …

Alongside the development of Internet of Things (IoT), security attacks are also increasing
day by day. A number of centralized attack detection mechanisms have been proposed to …

With the recent increase in internet usage, the number of important, sensitive, confidential
individual and corporate data passing through internet has increasingly grown. With gaps in …

One key issue in text mining and natural language processing is how to effectively represent
documents using numerical vectors. One classical model is the Bag-of-Words (BoW). In a …

Fuzzy sets emerged in 1965 in a paper by Lotfi Zadeh. In 1969 Ruspini published a seminal
paper that has become the basis of most fuzzy clustering algorithms. His ideas established …

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 …

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 …

Domain knowledge can be introduced into fuzzy clustering with the aid of information
granules, embodied by the concept of viewpoints. For such kind of fuzzy clustering methods …