In the context of big-data analysis, the clustering technique holds significant importance for
the effective categorization and organization of extensive datasets. However, pinpointing the …

Ensemble clustering (EC) gains more and more attention because it can improve the
effectiveness and robustness of single clustering methods. A popular ensemble approach is …

Abstract Interval Type-2 Fuzzy K-means (IT2FKM) is an efficient clustering algorithm, which
mainly focuses on further describes the uncertainty of the data sets by introducing interval …

Diversity and quality are two important factors that affect clustering ensemble performance.
Some base clusterings are irrelevant and redundant, which decreases the performance of …

This paper presents an innovative task allocation method for multi-robot systems that aims to
optimize task distribution while taking into account various performance metrics such as …

With the increase in the number of high-dimensional data, the characteristic phenomenon of
unbalanced distribution is increasingly presented in various big data applications. At the …

This paper presents a novel unsupervised deep learning methodology for the analysis of
self-assembled structures formed in evaporating droplets. The proposed approach focuses …

Cluster analysis constitutes a pivotal component of database mining, with DBSCAN being
one of the most extensively employed algorithms in this domain. Nevertheless, DBSCAN is …

Hierarchical clustering is able to provide partitions of different granularity levels. However,
most existing hierarchical clustering techniques perform clustering in the original feature …

Clustering ensembles can obtain more superior final results by combining multiple different
clustering results. The qualities of the points, clusters, and partitions play crucial roles in the …