The clustering of categorical data is a common and important task in computer science,
offering profound implications across a spectrum of applications. Unlike purely numerical …

Numerous real-world applications apply categorical data clustering to find hidden patterns in
the data. The K-modes-based algorithm is a popular algorithm for solving common issues in …

Multi-view clustering, which optimally integrates complementary information from different
views to improve clustering performance, has drawn considerable attention in recent years …

Distance calculation is straightforward when working with pure categorical or pure numerical
data sets. Defining a unified distance to improve the clustering performance for a mixed data …

IP Addresses are a central part of packet-and flow-based network data. However,
visualization and similarity computation of IP Addresses are challenging to due the missing …

In clustering analysis, data attributes may have different contributions to the detection of
various clusters. To solve this problem, the subspace clustering technique has been …

Class imbalance problem has been extensively studied in the recent years, but imbalanced
data clustering in unsupervised environment, that is, the number of samples among clusters …

Heterogeneous attribute data composed of attributes with different types of values are quite
common in a variety of real-world applications. As data annotation is usually expensive …

Clustering is one of the most commonly used techniques for unsupervised data analysis. As
real data sets are usually composed of numerical and categorical features that are …

An industrial mobile network is crucial for industrial production in the Internet of Things. It
guarantees the normal function of machines and the normalization of industrial production …