Given A Set Of Objects And A Pairwise Similarity Measure Between Them, The Goal Of Correlation Clustering Is To Partition The Objects In A Set Of Clusters To Maximize The Similarity Of The Objects Within The Same Cluster And Minimize The Similarity Of The Objects In Different Clusters. In Most Of The Variants Of Correlation Clustering, The Number Of Clusters Is Not A Given Parameter; Instead, The Optimal Number Of Clusters Is Automatically Determined. Correlation Clustering Is Perhaps The Most Natural Formulation Of Clustering: As It Just Needs A Definition Of Similarity, Its Broad Generality Makes It Applicable To A Wide Range Of Problems In Different Contexts, And, Particularly, Makes It Naturally Suitable To Clustering Structured Objects For Which Feature Vectors Can Be Difficult To Obtain.