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 …

To understand the intricacies of microorganisms at the molecular level requires making
sense of copious volumes of data such that it may now be humanly impossible to detect …

Within the vast expanse of computerized language processing, a revolutionary entity known
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …

Clustering validation and identifying the optimal number of clusters are crucial in expert and
intelligent systems. However, the commonly used cluster validity indices (CVI) are not …

This paper describes a focused literature survey of self-organizing maps (SOM) in support of
intrusion detection. Specifically, the SOM architecture can be divided into two categories, ie …

Concepts have been adopted in concept-cognitive learning (CCL) and conceptual clustering
for concept classification and concept discovery. However, the standard CCL algorithms are …

Clustering algorithms have become one of the most critical research areas in multiple
domains, especially data mining. However, with the massive growth of big data applications …

With the advancement of web 2.0 and the development of the Internet of Things (IoT), all
tasks can be handled with the help of handheld devices. Web APIs or web services are …

In human concept learning, people can naturally combine a handful of labeled data with
abundant unlabeled data when they make classification decisions, which is also known as …

In most European countries, residential assets account for as much as 85% of the building
stock floor area and are, on average, very outdated and energy inefficient. Moreover, the …