Data mining plays an important role in various human activities because it extracts the
unknown useful patterns (or knowledge). Due to its capabilities, data mining become an …

Detecting communities in dynamic evolving networks is of great interest. It has received
tremendous attention from researchers. One promising solution is to update communities …

Discovering evolving communities in dynamic networks is essential to important applications
such as analysis for dynamic web content and disease progression. Evolutionary clustering …

Co-clustering refers to the simultaneous clustering of objects and their features. It is used as
a clustering technique when the data exhibit similarities only in a subset of features instead …

In this paper, we present a novel graph data model to analyze eating habits and physical
activities of a large number of persons, aiming at automatically detect groups of users …

Graph co-clustering aims to simultaneously group heterogeneous vertices in bipartite
networks. The current algorithms measure similarity of vertices by either topology or latent …

Co-clustering ensemble establishes a consensus co-clustering over the data, and the
ensemble process can be described as an optimization problem that can be solved by …

Many networks in society and nature are dynamic, and identifying evolving communities in
dynamic networks sheds light on revealing the structure and function of the overall systems …

Clustering analysis aims to group a set of similar data objects into the same cluster. Topic
models, which belong to the soft clustering methods, are powerful tools to discover latent …

This paper introduces the concept of pattern systems that evolve, with a focus on scripts, a
specific type of pattern system. The study analyses the development of different script …