Validation is one of the most important aspects of clustering, particularly when the user is
designing a trustworthy or explainable system. However, most clustering validation …

Online clustering has attracted attention due to the explosion of ubiquitous continuous
sensing. Streaming clustering algorithms need to look for new structures and adapt as the …

ClusterValidityIndices. jl is a Julia package for evaluating the performance of clustering
algorithms without the aid of supervised labels. Cluster Validity Indices (CVI) provide a …

External cluster validity indices (CVIs) are used to quantify the quality of a clustering by
comparing the similarity between the clustering and a ground truth partition. However, some …

To evaluate clustering results is a significant part of cluster analysis. There are no true class
labels for clustering in typical unsupervised learning. Thus, a number of internal evaluations …

Because clustering is an unsupervised learning task, a number of different validity indices
have been proposed to measure the quality of the clustering results. However, there is no …

Clustering is one of the main tasks of machine learning. Internal clustering validation
indexes (CVIs) are used to measure the quality of several clustered partitions to determine …

In streaming data applications, the incoming samples are processed and discarded, and
therefore, intelligent decision-making is crucial for the performance of lifelong learning …

In unsupervised learning tasks, one of the most significant and challenging aspects is how to
estimate the optimal number of clusters (NC) for a particular set of data. Identifying NC in a …

Cluster validity evaluation is a hot issue in clustering algorithm research. Aiming at
determining the optimal number of clusters in cluster validity evaluation, this paper proposes …