Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

With the widespread deployment of sensors and the Internet-of-Things, multi-view data has
become more common and publicly available. Compared to traditional data that describes …

Multi-view clustering (MVC) aims to fuse the information among multiple views to achieve
effective clustering. Many MVC algorithms based on semi-nonnegative matrix factorization …

Multi-view learning or learning with multiple distinct feature sets is a rapidly growing
direction in machine learning with well theoretical underpinnings and great practical …

Clustering with multiview data is becoming a hot topic in data mining, pattern recognition,
and machine learning. In order to realize an effective multiview clustering, two issues must …

Abstract Multi-View Clustering (MVC) is an emerging research area aiming to cluster
multiple views of the same data, which has recently drawn substantial attention. Various …

Process mining refers to the discovery, conformance, and enhancement of process models
from event logs currently produced by several information systems (eg workflow …

Multi-view clustering, which aims to group data with multiple views, has recently attracted
intense research attention. Text documents bring additional difficulties to multi-view …

For a given data set, exploring their multi-view instances under a clustering framework is a
practical way to boost the clustering performance. This is because that each view might …

Multi-view clustering is an important and fundamental problem. Many multi-view subspace
clustering methods have been proposed, and most of them assume that all views share a …