Partitioning-based methods divide n objects into k partitions where k is smaller than or equal to n. In partitioning-based techniques, each partition contains at least one object and each object belongs to only one partition. Objects are divided hierarchically in hierarchical- based clustering. Hierarchical-based clustering methods can be classified as agglomerative and divisive approaches. Initially, each object represents a cluster in agglomerative approaches, then close clusters are merged consecutively until a termination condition holds or all objects or clusters are collected into one cluster. In divisive approaches, all objects are grouped into one cluster. Then, a cluster is divided into smaller clusters in each consecutive step until each object is assigned to one cluster or a termination condition holds. Density-based clustering methods group objects into clusters based on densities of clusters to represent clusters with arbitrary shape. An object in a cluster must have at least minimum number of neighbors in the neighborhood of a given radius. In model-based methods, objects are assigned to clusters based on defined models of clusters. Therefore, the correct option is C.