Explain the concept of decision trees in classification. Provide an example of building and visualizing a decision tree using R. How can K-means clustering be applied to a dataset in R?

Decision trees are a fundamental concept in data science, particularly in the context of classification problems. They are supervised learning models that are used to make decisions by dividing data into smaller subsets based on feature values. The goal of a decision tree in classification is to split the data into different classes by making a series of decisions, represented as branches of the tree, based on the features of the data.

### Decision Trees in Classification

A decision tree works by recursively splitting a dataset into subsets using a feature and a threshold that best separates the data into different classes. The decision-making process starts at the root node, which represents the entire dataset, and continues down to the leaf nodes, where the final classification decision is made.

The key components of a decision tree include:

1. Root Node: The topmost node representing the entire dataset.
2. Internal Nodes: Represent the decision points based on feature values.
3. Edges: Represent the outcomes of decisions, leading to further splits or to the final classification.
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