  Clustering is an unsupervised machine learning method grouping data samples
into clusters of similar objects. In practice, clustering has been used in
numerous applications such as banking customers profiling, document retrieval,
image segmentation, and e-commerce recommendation engines. However, the
existing clustering techniques present significant limitations, from which is
the dependability of their stability on the initialization parameters (e.g.
number of clusters, centroids). Different solutions were presented in the
literature to overcome this limitation (i.e. internal and external validation
metrics). However, these solutions require high computational complexity and
memory consumption, especially when dealing with big data. In this paper, we
apply the recent object detection Deep Learning (DL) model, named YOLO-v5, to
detect the initial clustering parameters such as the number of clusters with
their sizes and centroids. Mainly, the proposed solution consists of adding a
DL-based initialization phase making the clustering algorithms free of
initialization. Two model solutions are provided in this work, one for isolated
clusters and the other one for overlapping clusters. The features of the
incoming dataset determine which model to use. Moreover, The results show that
the proposed solution can provide near-optimal clusters initialization
parameters with low computational and resources overhead compared to existing
solutions.
