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 supervised machine learning technique used primarily for classification and regression tasks. In the context of classification, decision trees aim to predict the class of a target variable by learning decision rules inferred from the input features. The structure of a decision tree resembles a flowchart, where each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to __________ _________ _______ ________ _______ __________ ___ ___ _________ _____.
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