Cluster analyzes have been widely used in mental health research to decompose inter-
individual heterogeneity by identifying more homogeneous subgroups of individuals …

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 …

The problem of missing values has long been studied by researchers working in areas of
data science and bioinformatics, especially the analysis of gene expression data that …

The work presented in this paper aims to develop new imputation methods to better handle
missing values encountered in astronomical data analysis, especially the classification of …

It is commonly agreed that the quality of data analysis may be degraded by the presence of
missing data. In various domains such as bioinformatics, an effective tool is required for the …

An enhanced cosine-based visual technique for the robust tweets data clustering | Emerald
Insight Books and journals Case studies Expert Briefings Open Access Publish with us …

With the frequent occurrence of network security events, the intrusion detection system will
generate alarm and log records when monitoring the network environment in which a large …

Social networks are the rich sources to people for sharing the knowledge on health-related
issues. Nowadays, Twitter is one of the great significant social platforms to the people for a …

As part of celebrating the success of OWA operators and their contributions over the past
decades, this work presents an original investigation of exploiting OWA in dealing with …

Topic models are widely used in building clusters of documents for more than a decade, yet
problems occurring in choosing the optimal number of topics. The main problem is the lack …