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 …

Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …

Modern artificial intelligence (AI) applications require large quantities of training and test
data. This need creates critical challenges not only concerning the availability of such data …

Unlike traditional unsupervised clustering, semi-supervised clustering allows users to
provide meaningful structure to the data, which helps the clustering algorithm to match the …

We offer a systematic analysis of the use of deep learning networks for stock market analysis
and prediction. Its ability to extract features from a large set of raw data without relying on …

Metagenomic datasets contain billions of protein sequences that could greatly enhance
large-scale functional annotation and structure prediction. Utilizing this enormous resource …

We survey agglomerative hierarchical clustering algorithms and discuss efficient
implementations that are available in R and other software environments. We look at …

The smart grid (SG) paradigm is the next technological leap of the conventional electrical
grid, contributing to the protection of the physical environment and providing multiple …

The goal of this paper is speaker diarisation of videos collected'in the wild'. We make three
key contributions. First, we propose an automatic audio-visual diarisation method for …

We experiment with two recent contextualized word embedding methods (ELMo and BERT)
in the context of open-domain argument search. For the first time, we show how to leverage …