A Deep Clustering Baseline Method Based on Contrastive Learning
Clustering,a vital unsupervised learning tool,plays a key role in fields such as data mining and image processing.A novel architecture for deep clustering is proposed,comprising contrastive instance generation networks,instance-level contrastive learning networks,and cluster-level contrastive learning networks.This design optimizes feature extraction and clustering through data augmentation and feature mapping.Experiments on four image datasets demonstrate the model's superior performance in clustering accuracy,normalized mutual information,and adjusted Rand index,especially in high-dimensional datasets.The research further confirms the model's flexibility and applicability under various improvement schemes,establishing its leading position in the field of deep clustering.
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