Clustering by Similarity Aggregation
Clustering by similarity aggregation is also known as relational clustering or Condorcet method which compares each data point with all other data points in pairs. For a pair of values A and B, these values are assigned to both the vectors m(A, B) and d(A, B). A and B are the same in m(A, B) but different in d(A, B).
where, S is the cluster
With the first condition, the cluster is constructed and with the next condition, the global Condorcet criterion is calculated. It follows in an iterative manner until specified iterations are not completed or the global Condorcet criterion produces no improvement.
Clustering in R Programming
Clustering in R Programming Language is an unsupervised learning technique in which the data set is partitioned into several groups called clusters based on their similarity. Several clusters of data are produced after the segmentation of data. All the objects in a cluster share common characteristics. During data mining and analysis, clustering is used to find similar datasets.