  With the booming development of data science, many clustering methods have
been proposed. All clustering methods have inherent merits and deficiencies.
Therefore, they are only capable of clustering some specific types of data
robustly. In addition, the accuracies of the clustering methods rely heavily on
the characteristics of the data. In this paper, we propose a new clustering
method based on the morphological operations. The morphological dilation is
used to connect the data points based on their adjacency and form different
connected domains. The iteration of the morphological dilation process stops
when the number of connected domains equals the number of the clusters or when
the maximum number of iteration is reached. The morphological dilation is then
used to label the connected domains. The Euclidean distance between each data
point and the points in each labeled connected domain is calculated. For each
data point, there is a labeled connected domain that contains a point that
yields the smallest Euclidean distance. The data point is assigned with the
same labeling number as the labeled connected domain. We evaluate and compare
the proposed method with state of the art clustering methods with different
types of data. Experimental results show that the proposed method is more
robust and generic for clustering two-dimensional or three-dimensional data.
