In the big data era, the data are generated from different sources or observed from different
views. These data are referred to as multi-view data. Unleashing the power of knowledge in …

As we all know, multiview clustering has become a hot topic in machine learning and pattern
recognition. Nonnegative matrix factorization (NMF) has been one popular tool in multiview …

Multi-view clustering can explore consistent information from different views to guide
clustering. Most existing works focus on pursuing shallow consistency in the feature space …

This paper addresses the problem of clustering ensemble which aims to combine multiple
clusterings into a probably better solution in terms of robustness, novelty and stability. The …

Studies the problem of maintaining materialized views of graph structured data. The base
data consists of records containing identifiers of other records. The data could represent …

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 …

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

Collaborative multiview clustering methods can efficiently realize the view fusion by
exploring complementary and consistent information among multiple views. However, these …

Massive data can create a real competitive advantage for the companies; it is used to better
respond to customers, to follow the behavior of consumers, to anticipate the evolutions, etc …

Multi-view subspace clustering is the most widespread method of multi-view clustering.
However, most existing multi-view subspace clustering approaches posit that high …