Data clustering is a commonly used data processing technique in many fields, which divides
objects into different clusters in terms of some similarity measure between data points …

Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …

This paper focuses on scalability and robustness of spectral clustering for extremely large-
scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra …

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 …

Big data has become an important issue for a large number of research areas such as data
mining, machine learning, computational intelligence, information fusion, the semantic Web …

Recently deep learning has been successfully adopted in many applications such as
speech recognition and image classification. In this work, we explore the possibility of …

In this paper, we address the problem of large-scale multi-view spectral clustering. In many
real-world applications, data can be represented in various heterogeneous features or …

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately,
due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of …

Digital-twin-enabled cloud-centric architecture is a promising evolution trend of sixth
generation (6G) network, which brings new opportunities and challenges for mobile video …

We present a comprehensive, detailed review of time-series data analysis, with emphasis on
deep time-series clustering (DTSC), and a case study in the context of movement behavior …