Data mining for the internet of things: literature review and challenges

F Chen, P Deng, J Wan, D Zhang… - International …, 2015 - journals.sagepub.com
The massive data generated by the Internet of Things (IoT) are considered of high business
value, and data mining algorithms can be applied to IoT to extract hidden information from …

Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature

AE Ezugwu, AK Shukla, MB Agbaje… - Neural Computing and …, 2021 - Springer
Cluster analysis is an essential tool in data mining. Several clustering algorithms have been
proposed and implemented, most of which are able to find good quality clustering results …

A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm

C Shi, B Wei, S Wei, W Wang, H Liu, J Liu - EURASIP journal on wireless …, 2021 - Springer
Clustering, a traditional machine learning method, plays a significant role in data analysis.
Most clustering algorithms depend on a predetermined exact number of clusters, whereas …

Clustering algorithms: A comparative approach

MZ Rodriguez, CH Comin, D Casanova, OM Bruno… - PloS one, 2019 - journals.plos.org
Many real-world systems can be studied in terms of pattern recognition tasks, so that proper
use (and understanding) of machine learning methods in practical applications becomes …

Machine learning: an accelerator for the exploration and application of advanced metal-organic frameworks

R Du, R ** and density
H Li, J Liu, K Wu, Z Yang, RW Liu, N **ong - IEEE Access, 2018 - ieeexplore.ieee.org
Automatic identification systems (AISs) serve as a complement to radar systems, and they
have been installed and widely used onboard ships to identify targets and improve …