Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

With advances in information acquisition technologies, multi-view data become ubiquitous.
Multi-view learning has thus become more and more popular in machine learning and data …

Fuzzy c-means clustering (FCM) with spatial constraints (FCM/spl I. bar/S) is an effective
algorithm suitable for image segmentation. Its effectiveness contributes not only to the …

Time series clustering is an essential unsupervised technique in cases when category
information is not available. It has been widely applied to genome data, anomaly detection …

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 …

While fuzzy c-means is a popular soft-clustering method, its effectiveness is largely limited to
spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to …

In this paper, a brain-like navigation scheme based on fuzzy kernel C-means (FKCM)
clustering assisted pyramid Lucas Kanade (LK) optical flow algorithm is developed to …

In this paper we present a comparison among some nonhierarchical and hierarchical
clustering algorithms including SOM (Self-Organization Map) neural network and Fuzzy c …

In this study, we present a comprehensive comparative analysis of kernel-based fuzzy
clustering and fuzzy clustering. Kernel based clustering has emerged as an interesting and …

The introduction of domain knowledge opens new horizons to fuzzy clustering. Then
knowledge-driven and data-driven fuzzy clustering methods come into being. To address …