Clustering has primarily been used as an analytical technique to group unlabeled data for
extracting meaningful information. The fact that no clustering algorithm can solve all …

Skewed class proportions in real-world datasets present a challenge for machine learning
algorithms, as they have a tendency to correctly categorize the majority class while …

This paper proposes a novel framework for clustering mixed numerical and categorical data
with missing values. It integrates the imputation and clustering steps into a single process …

As new generations of technology appear, legacy knowledge management solutions and
applications become increasingly out of date, necessitating a paradigm shift. Machine …

This paper explores the problem of multi-view clustering, which aims to promote clustering
performance with multi-view data. The majority of existing methods have problems with …

Appropriately handling the scalability of clustering is a long-standing challenge for the study
of clustering techniques and is of fundamental interest to researchers in the community of …

In order to alleviate the network information overload, recommender system becomes
widespread in personalized recommendation. However, due to the increase of the number …

Multilayer networks are used to encode multiple types of relations arising in complex
systems and have received significant attention in recent years. Community detection in …

Most of the existing graph-based clustering models performed clustering by adopting a two-
stage strategy which first completes the spectral embedding from a given fixed graph and …

Ships' routeing was first adopted to reduce the risk of ship collisions and promote marine
traffic efficiency. But as maritime transport continues to develop, the number of ships passing …