Optimizing the prototypes with a novel data weighting algorithm for enhancing the classification performance of fuzzy clustering
Fuzzy clustering is regarded as an unsupervised learning process that constitutes a
prerequisite for many other data mining techniques. Deciding how to classify data efficiently …
prerequisite for many other data mining techniques. Deciding how to classify data efficiently …
Augmentation of the reconstruction performance of fuzzy C-means with an optimized fuzzification factor vector
Abstract Information granules have been considered as the fundamental constructs of
Granular Computing. As a useful unsupervised learning technique, Fuzzy C-Means (FCM) is …
Granular Computing. As a useful unsupervised learning technique, Fuzzy C-Means (FCM) is …
Augmentation of Soft Partition with a Granular Prototype Based Fuzzy C-Means
R Wang, K Xu, Y Wang - Mathematics, 2024 - mdpi.com
Clustering is a fundamental cornerstone in unsupervised learning, playing a pivotal role in
various data mining techniques. The precise and efficient classification of data stands as a …
various data mining techniques. The precise and efficient classification of data stands as a …
From granulation-degranulation mechanisms to fuzzy rule-based models: Augmentation of granular-based models with a double fuzzy clustering
In this study, we develop a novel clustering with double fuzzy factors to enhance the
performance of the granulation-degranulation mechanism, with which a fuzzy rule-based …
performance of the granulation-degranulation mechanism, with which a fuzzy rule-based …