… Clustering algorithms Many clustering algorithms are proposed in the data mining community,
and for each method, different variations are developed. In the power system literature, …

… On the contrary, a data-driven dynamic clustering algorithm would be much more efficient …
a dynamic clustering algorithm in this paper. Machine learning and big data utilization to invent …

… Data is divided into many clusters, and then the cluster size is adjusted in the second stage.
In Algorithm 1, the appropriate procedures for data clustering … cluster may fluctuate once data …

… A few algorithms require the data set input dimension to be n × d v , we will emphasize
this difference in these algorithms. { x i v } i = 1 n is the set of samples in the vth view. Identity …

… algorithm that has learned how to rank web pages. These algorithms are used for various
purposes like data … learning is that, once an algorithm learns what to do with data, it can do its …

… similar to others in the cluster and distinct from the individuals in other clusters. The main
aim of this study was to see whether data-driven algorithms would segregate narcolepsy type 1 …

… clustering accuracy. To solve these problems, a generalized deep learning clustering (GDLC)
algorithm … Firstly, a nonlinear constrained NMF (NNMF) algorithm is constructed to achieve …

… A new method for distinguishing the potential optimal or most appropriate cluster number
used in the clustering algorithm is proposed in this paper. We exploited the interaction angle of …

… data. Therefore, performing the multiview graph clustering with a simple yet high efficient
algorithm … In this paper, we simultaneously deal with three issues of multiview spectral clustering…

… Cluster algorithms are gaining in popularity in biomedical research due to their compelling
ability to identify discrete subgroups in data… clustering algorithms provide a method for the data…