The goal of information retrieval (IR) systems is to find the contents most closely related to
the user's information needs from a pool of information. However, conventional IR methods …

Clustering incomplete instance is still a challenging task since missing values maybe make
the cluster information ambiguous, leading to the uncertainty and imprecision in results. This …

Zero trust architecture (ZTA) is a paradigm shift in how we protect data, stay connected and
access resources. ZTA is non-perimeter-based defence, which has been emerging as a …

Multi-view clustering (MVC) holds a significant role in domains like machine learning, data
mining, and pattern recognition. Despite the development of numerous new MVC …

Knowing the number of clusters a priori is one of the most challenging aspects of
unsupervised learning. Clustering Internal Validity Indices (CIVIs) evaluate partitions in …

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 …

Rapid advances in infrastructure health monitoring and sensing technologies allow the
monitoring of infrastructure assets continuously and in real-time throughout their life span …

Fashion image clustering is the key to fashion retrieval, forecasting, and recommendation
applications. Manual labeling-based clustering is both time-consuming and less accurate …

The clustering problem is one of the most studied and challenging in machine learning, as it
attempts to identify similarities within data without any prior knowledge. Among modern …

The amount of information available on the internet is growing exponentially. The majority of
this information is ambiguous by nature, and information retrieval (IR) systems typically …