In a dynamic environment, the data collected from real applications varies not only with the
amount of objects but also with the number of features, which will result in continuous …

This paper focuses on the unsupervised learning problem within membrane computing, and
proposes an innovative solution inspired by membrane computing techniques, the fuzzy …

With the rapid development of sensors and mechanical systems, we produce an
exponentially large amount of data daily. Usually, faults are prevalent in these sensory …

Incremental learning has become a new research hotspot in the field of machine learning.
Compared with traditional machine learning, incremental learning can continuously learn …

Automatic clustering problem, which needs to detect the appropriate clustering without a pre-
defined number of clusters (k), is difficult and challenging in unsupervised learning owing to …

To deal with the uncertainty, vagueness and overlap** distribution within the data sets, a
novel incremental fuzzy cluster ensemble method based on rough set theory (IFCERS) is …

Categorical data clustering algorithms, in contrast to numerical ones, are still in their infancy
despite some algorithms have been proposed in the literature. It is known that many …

Evolutionary multiobjective clustering (MOC) algorithms have shown promising potential to
outperform conventional single-objective clustering algorithms, especially when the number …

Abstract Nonnegative Matrix Factorization (NMF) and Concept Factorization (CF) are two
popular methods for finding the low-rank approximation of nonnegative matrix. Different from …

Categorical data clustering is a difficult and challenging task due to the special characteristic
of categorical attributes: no natural order. Thus, this study aims to propose a two-stage …