Data Mining Techniques: A Survey and Comparative Analysis in Vehicular Ad Hoc Networks

Vehicular Ad hoc Networks (VANETs) are highly mobile wireless networks that play a crucial role in public safety dispatches and commercial operations. Recent advancements in VANETs have enabled the integration of data generated from these networks into smart operations, providing quality of life services. Data mining is a process that involves extracting valuable patterns and information from data. One promising area of research involves applying data mining techniques to VANETs to extract useful patterns. This paper presents an overview of basic data mining techniques, including pre-processing, outlier detection, clustering, and data ordering. Additionally, this paper describes the most commonly used classification and clustering techniques, comparing them based on their strengths and weaknesses.