# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 3 Artificial Intelligence
### Regression Algorithms
#### 3.1.2 Unsupervised Learning  
Unsupervised Learning investigates unlabeled datasets for patterns. In contrast to SL, the absence of pre-labeled input-output pairs eliminates human oversight. Unsupervised methods utilize characteristics such as orthogonality, correlations, statistical separability, and so forth to self-infer relationships between variables. Clustering or classification techniques, on key evaluation elements, are, although not entirely, the most prevalent unsupervised methods. We are starting to rely on unsupervised DNN-based techniques, including Generative Adversarial Networks (GANs) [26]. Unsupervised Learning performs the main tasks based on three categories, clustering, association, and dimensionality, as discussed below. 1. The K-means algorithm is an unsupervised non-hierarchical clustering algorithm that is part of a specifically exclusive method. By employing this approach, the input knowledge can be classified between k different clusters [65]. As a result, data that are identical will be located in the corresponding cluster. Furthermore, it is imperative to underscore how the knowledge being analyzed is limited to a single cluster at any moment, indicating limited merging [66]. Therefore, a single fact cannot simultaneously belong to two different clusters. The application of this methodology offers benefits when dealing with confidential information as it can function despite the need for annotated data. As a result, it ensures the reliability of VANET clustering effectively, bolsters the protection of hash operations when confidential data are transmitted, and is well suited to be utilized in congestion discovery [67]. Fuzzy K-means is an overlapping technique of Unsupervised Learning with the soft-hand approach. 2. Hierarchical clustering [HC] begins by considering the data points as distinct clusters. Subsequently, they are progressively merged or divided according to their similarity or dissimilarity. This approach involves the division of terminals into groups based on both geographical and social affinity; hierarchical clustering algorithms facilitate the efficient transmission of messages inside every group [61; 68]. This process generates a dendrogram, which resembles a tree. Two distinct types of agglomerative hierarchical clustering were examined in this research. It has two main parts, agglomerative and divisive. Agglomerative is a bottom-up approach that includes four distance methods for measuring similarity, i.e., word linkage, average linkage, complete linkage, and single linkage. All these techniques can be calculated with the help of Euclidean distance. Divisive clustering is a top-down approach [61]. 3. GMM clustering is a probabilistic clustering method used to solve density estimation or soft clustering problems and operates under the assumption that observations are produced by combining a finite set of Gaussian variables. Introducing covariance into the issue, this probabilistic model synthesizes the uncertainty of cluster assignments modeled with k-means [69]. The objective of this approach is to represent a given dataset as a combination of numerous Gaussian probability distributions. More precisely, when provided with a dataset consisting of N observations denoted as \(\times\)1, \(\times\)2, \(\ldots\), \(\times\)N, the GMM clustering function aims at determining the variables of K Gaussian distributions that may accurately represent the data [31]. By manipulating the quantity of Gaussian mixtures, GMM is capable of fitting arbitrary distributions, enabling it to accurately characterize the impact degradation process [70]. 4. MC is a stochastic model that specifies a possible order of instances so that the present situation alone determines the probability of each event. The possible states of an MC with finite states are discrete, or countable. The MC operates in a memoryless manner, as the transition from one state to the next is exclusively determined by the current state and the associated transition probabilities [71].