Maybe you can start from here: Article Tree-Based Clustering and Extensions

There are more interesting chapters in the Comprehensive Chemometrics book that relate to what you're looking for.

Chapter 2.27 - Common Clustering Algorithms

Chapter Multivariate Approaches to Classification using Genetic Algorithms

Thanks.

If you have these Articles ,please could you send it to me ?

Maybe you should try sci-hub ;)

For understanding and using machine learning algorithms like J48, Random Forest, and Random Tree, particularly in the context of misbehavior detection in VANET (Vehicular Ad-hoc Networks), the following references might be helpful:

1. Random Forests and Decision Trees: This study explains the efficacy of Random Forest as a machine learning algorithm for classification tasks. Random Forest builds multiple decision tree models during training and combines their predictions. It is particularly effective for handling a variety of classification challenges, making it a suitable choice for misbehavior detection in networks like VANETs.

Article Random Forests and Decision Trees

2. Comparative Study of J48 and Other Decision Trees: This research compares various decision tree algorithms including CART, J48graft, J48, ID3, Decision Stump, and Random Forest. It discusses how J48 works as a predictive model by deciding the dependent variable based on available data, making it useful for classification problems in network security scenarios. The study also elaborates on the Random Forest's method of combining multiple decision trees for classification and regression, which can be valuable for analyzing and predicting network behavior.

Article CART, J-48graft, J48, ID3, Decision Stump and Random Forest:...

3. Application in Fake News Classification: Although not directly related to VANET, this study uses Random Forest and Decision Tree (J48) classifiers for fake news detection. It gives insights into how these classifiers can be utilized in complex datasets to distinguish between different classes, in this case, real and fake news. Such methodologies can be adapted for misbehavior detection in VANETs, where the goal is to differentiate between normal and anomalous behavior.

Article Fake News Classification Using Random Forest and Decision Tree (J48)

These references provide a foundational understanding of how these algorithms function and how they can be applied in different contexts, including network security and misbehavior detection. Given the complexity and dynamism of VANETs, employing machine learning algorithms like J48 and Random Forest can be effective for identifying and addressing misbehaviors in the network.

Hi @Izdihar Shaleesh,

You can check several references about this topic in this paper

Article Hybrid approaches to optimization and machine learning metho...

You can visit my Research gate profile for similar research, which may help you to get an about about these algorithms