AGNES begins by recognising that every data point will have its own cluster, and even if there are n data rows, the algorithm will start with n clusters. Then, iteratively, clusters that are the most similar are joined to form a larger cluster, depending on the distances measured in DIANA. Iterations are carried out until we get a single large cluster containing all of the data points. Advantages: 1. Although the user must define a division threshold, no prior knowledge of the number of clusters is required. 2. Simple to apply across a variety of data types and known to produce reliable results for data obtained from a variety of sources. As a result, it has a wide range of applications. Disadvantages: 1. Cluster division (DIANA) or combination (AGNES) is quite rigorous, and once done, it cannot be reversed or re-assigned in subsequent iterations or re-runs. 2. It has a high temporal complexity for all n data points, in the order of O(n^2logn), and so cannot be utilised for larger datasets. 3. Unable to deal with outliers and noise