Information granules, as encountered in natural language, are implicit in nature. To make
them fully operational so they can be effectively used to analyze and design intelligent …

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 …

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 …

Uncertainty and fuzziness generally exist in real-life data. Approximations are employed to
describe the uncertain information approximately in rough set theory. Certain and uncertain …

Rough sets, especially fuzzy rough sets, are supposedly a powerful mathematical tool to
deal with uncertainty in data analysis. This theory has been applied to feature selection …

Data clustering is an important and frequently used unsupervised learning method. Recent
research has demonstrated that incorporating instance-level background information to …

This paper presents a novel algorithm for training radial basis function (RBF) networks, in
order to produce models with increased accuracy and parsimony. The proposed …

Knowledge-based clustering algorithms can improve traditional clustering models by
introducing domain knowledge to identify the underlying data structure. While there have …

With the rapid development of uncertain artificial intelligent and the arrival of big data era,
conventional clustering analysis and granular computing fail to satisfy the requirements of …

Fuzzy clustering algorithms are usually data-driven. Recently, knowledge has been
introduced into these methods to form knowledge-driven and data-driven fuzzy clustering …