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 …

A defining aspect of brain organization is its spatial heterogeneity, which gives rise to
multiple topographies at different scales. Brain parcellation—defining distinct partitions in …

Clustering is a long-standing important research problem, however, remains challenging
when handling large-scale image data from diverse sources. In this paper, we present a …

A graph is usually formed to reveal the relationship between data points and graph structure
is encoded by the affinity matrix. Most graph-based multiview clustering methods use …

The modern science of networks has made significant advancement in the modeling of
complex real-world systems. One of the most important features in these networks is the …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

In recent years, concept-based approaches have emerged as some of the most promising
explainability methods to help us interpret the decisions of Artificial Neural Networks (ANNs) …

Graph-based clustering methods perform clustering on a fixed input data graph. If this initial
construction is of low quality then the resulting clustering may also be of low quality …

Community detection is a popular yet thorny issue in social network analysis. A symmetric
and nonnegative matrix factorization (SNMF) model based on a nonnegative multiplicative …