A Deep Learning Object Detection Method for an Efficient Clusters Initialization

TL;DR

This paper introduces a deep learning-based method using YOLO-v5 to automatically determine initial clustering parameters, reducing computational costs and improving stability for various clustering applications.

Contribution

It presents a novel deep learning approach for cluster initialization that eliminates the need for traditional, resource-intensive methods, applicable to both isolated and overlapping clusters.

Findings

Provides near-optimal initial cluster parameters

Reduces computational and memory overhead

Applicable to diverse clustering scenarios

Abstract

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.