In the last decades, a wide portfolio of Feature Weighting (FW) methods have been
proposed in the literature. Their main potential is the capability to transform the features in …

Neighborhood rough sets based attribute reduction, as a common dimension reduction
method, has been widely used in machine learning and data mining. Each attribute has the …

Анотація У монографії розвиваються структурні технології аналізу багатовимірних
даних в інтелектуальних системах. Основна увага приділяється ансамблевим моделям …

The fuzzy c-means (FCM) algorithm is a popular method for data clustering and image
segmentation. However, the main problem of this algorithm is that it is very sensitive to the …

Subspace techniques have gained much attention for their remarkable efficiency in
representing high-dimensional data, in which sparse subspace clustering (SSC) and low …

Abstract The Fuzzy C-means (FCM) clustering algorithm is an effective method for image
segmentation. Non-local spatial information considers more redundant information of the …

As a core paradigm of the evidential clustering algorithm, evidential c-means (ECM) offers a
more flexible credal partition to characterize uncertainty and imprecision in cluster …

Soft clustering, a widely utilized method in data analysis, offers a versatile and flexible
strategy for grouping data points. Most soft clustering algorithms assume that all the features …

The possibilistic fuzzy c-means clustering (PFCM) algorithm is a hybridization of possibilistic
c-means clustering (PCM) and fuzzy c-means clustering (FCM) algorithms. However, there …

The results of image classification problem solving using structural methods in computer
vision systems are presented. The technology for introducing fuzzy clustering on a set of …