One of the most fundamental approaches to learn and understand from any type of data is
by organizing it into meaningful groups (or clusters) and then analyzing them, which is a …

Controlling the impact of partial supervision on the outcomes of modeling is of uttermost
importance in semi-supervised fuzzy clustering. Semi-Supervised Fuzzy C-Means …

Acoustic features of speech are promising as objective markers for mental health monitoring.
Specialized smartphone apps can gather such acoustic data without disrupting the daily …

A new DNS-based anti-evasion technique for botnets detection is proposed. It is based on a
cluster analysis of the features obtained from the payload of DNS-messages. The method …

The inherent fuzziness of wetland ecosystems largely accounts for the spectral variability of
wetland ecosystems in remote sensing images. In addition, a limited spatial resolution leads …

In this study, we propose the semi-supervised particle swarm optimisation (ssPSO) algorithm
for data clustering. The algorithm takes advantage of the strengths of semi-supervised fuzzy …

This paper introduces Fuzzy HSS, a semisupervised hierarchical clustering approach that
uses fuzzy instance-level constraints. These constraints are external information on the …

Soria et al. have successfully identified six clinically useful and novel subgroups in the
Nottingham Tenovus Breast Cancer (NTBC) data set. However, the methodology used is …

Semi-supervised clustering algorithms aim at discovering the hidden structure of data sets
with the help of expert knowledge, generally expressed as constraints on the data such as …

Abstract The Nottingham Tenovus Breast Cancer data has been successfully classified into
six novel and clinically useful subgroups. But the existing technique used is semi manual. In …