Final answer:
K-means clustering is the correct algorithm for Ahmed to use to group unlabelled data based on similarities, as it is designed for unsupervised learning and automatically classifies data into clusters.
Step-by-step explanation:
Ahmed needs to group his unlabelled data according to its similarities to find the underlying structure of a dataset. The best machine learning algorithm for this task is K-means clustering. K-means clustering is a type of unsupervised learning used when you have unlabelled data (i.e., data without defined categories or groups) and you wish to find inherent patterns in the data. The algorithm works by finding groups in the data, with the number of groups represented by the variable K. The algorithm then iteratively assigns each data point to the nearest cluster, while keeping the centroids of each cluster as small as possible.
The other options such as Decision tree, Linear regression, and Support vector machine tend to have different applications. Decision trees are used for classification and regression tasks, Linear regression is used for predicting a quantitative response, and Support vector machines are mostly used for classification problems.
Considering the need to group unlabelled data by similarities, K-means clustering is the appropriate choice as it automatically classifies data into groups based solely on feature similarity.