Common clustering algorithms

The major steps of an overall clustering task are preclustering, clustering, and postclustering. Preclustering involves data preparation, including feature extraction, selection, transformation normalization, cleaning, and data reduction, whereas postclustering involves cluster usability encompassing cluster validity, reasoning, interpretation, and visualization. This article focuses on the second step, “clustering,” which is further divided into two key modules: clustering criterion and clustering method. This clustering step takes a set X = {x1, x2, …, xn} of preprocessed points (synonymously elements, objects, instances, cases or patterns) as an input and produces a clustered result as an output (either partitioning or hierarchical) for postclustering. It first requires a clustering criterion to be built and needs a clustering algorithm to optimize the clustering criterion