A Deep Learning Object Detection Method for an Efficient Clusters Initialization

• URL: http://arxiv.org/abs/2104.13634v2
• Date: Thu, 29 Apr 2021 07:03:27 GMT
• Title: A Deep Learning Object Detection Method for an Efficient Clusters Initialization
• Authors: Hassan N. Noura, Ola Salman, Rapha\"el Couturier, Abderrahmane Sider
• Abstract summary: Clustering has been used in numerous applications such as banking customers profiling, document retrieval, image segmentation, and e-commerce recommendation engines. Existing clustering techniques present significant limitations, from which is the dependability of their stability on the initialization parameters. This paper proposes a solution that can provide near-optimal clustering parameters with low computational and resources overhead.
• Score: 6.365889364810239
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 high dimensional 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 possible centroids. Mainly, the proposed solution consists of adding a DL-based initialization phase making the clustering algorithms free of initialization. The results show that the proposed solution can provide near-optimal clusters initialization parameters with low computational and resources overhead compared to existing solutions.

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