Clustering is a data analysis method for extracting knowledge by discovering
groups of data called clusters. Among these methods, state-of-the-art
density-based clustering methods have proven to be effective for
arbitrary-shaped clusters. Despite their encouraging results, they suffer to
find low-density clusters, near clusters with similar densities, and
high-dimensional data. Our proposals are a new characterization of clusters and
a new clustering algorithm based on spatial density and probabilistic approach.
First of all, sub-clusters are built using spatial density represented as
probability density function ($p.d.f$) of pairwise distances between points. A
method is then proposed to agglomerate similar sub-clusters by using both their
density ($p.d.f$) and their spatial distance. The key idea we propose is to use
the Wasserstein metric, a powerful tool to measure the distance between $p.d.f$
of sub-clusters. We show that our approach outperforms other state-of-the-art
density-based clustering methods on a wide variety of datasets.

通过提出新的聚类特征和一种基于空间密度和概率方法的聚类算法，本研究在广泛的数据集上展示出较其他先进的基于密度的聚类方法更好的性能。