Clustering is an important unsupervised classification technique where data points are
grouped such that points that are similar in some sense belong to the same cluster. Cluster …

Traditional prototype-based clustering methods, such as the well-known fuzzy c-means
(FCM) algorithm, usually need sufficient data to find a good clustering partition. If available …

Cluster‐scaled analysis means exploiting the cluster‐based scaling to conventional data
analysis to obtain more accurate results or results that we cannot obtain by using ordinary …

Most of kmeans-type clustering algorithms rely on only intra-cluster compactness, ie the
dispersions of a cluster. Inter-cluster separation which is widely used in classification …

In this paper we propose a new heuristic semi-supervised fuzzy co-clustering algorithm (SS-
HFCR) for categorization of large web documents. In this approach, the clustering process is …

Class imbalances have been reported to compromise the performance of most standard
classifiers, such as Naive Bayes, Decision Trees and Neural Networks. Aiming to solve this …

Virtual machines (VM) on a Cloud platform can be influenced by a variety of factors which
can lead to decreased performance and downtime, affecting the reliability of the Cloud …

In real-world problems we encounter situations where patterns are described by blocks
(families) of features where each of these groups comes with a well-expressed semantics …

Learning a proper distance for clustering from prior knowledge falls into the realm of
semisupervised fuzzy clustering. Although most existing learning methods take prior …

In this study, we introduce a certain knowledge-guided scheme of fuzzy clustering in which
domain knowledge is represented in the form of so-called viewpoints. Viewpoints capture a …