The k-means clustering algorithm is considered one of the most powerful and popular data
mining algorithms in the research community. However, despite its popularity, the algorithm …

For graph-based multi-view clustering, a critical issue is to capture consensus cluster
structures via a two-stage learning scheme. Specifically, first learn similarity graph matrices …

Previous spectral clustering methods sequentially conduct three steps, ie, similarity matrix
learning from original data, spectral representation learning, and K-means clustering on …

As a variant of non-negative matrix factorization (NMF), symmetric NMF (SymNMF) can
generate the clustering result without additional post-processing, by decomposing a …

A two-stage framework is presented for household electricity consumption pattern mining, in
which a concurrent k-means and spectral clustering (CKSC) method is used in the first …

Multi-view and ensemble clustering methods have been receiving considerable attention in
exploiting multiple features of data. However, both of these methods have their own set of …

As an effective method for clustering applications, the clustering ensemble algorithm
integrates different clustering solutions into a final one, thus improving the clustering …

The aim of this study is the development of a weighted consensus clustering that assigns
weights to single clustering methods using the purity utility function. In the case of Big data …

Hardware Trojans (HTs) have become a major threat for the integrated circuit industry and
supply chain and have motivated numerous developments of HT detection schemes …

Clustering ensemble can overcome the instability of clustering and improve clustering
performance. With the rapid development of clustering ensemble, we find that not all …