Abstarct: Abstract: The expansion and development of the web and the increase in digital information have intensified the need for robust and effective information retrieval (IR) systems. Query performance prediction (QPP) plays a pivotal role in identifying difficult queries and improving retrieval methods. However, existing QPP methods suffer from several limitations: (1) score-baxsed approaches cannot capture the structural relationships between retrieved documents; (2) supervised methods require labeled training data, making them costly and impractical for new domains; and (3) unsupervised post-retrieval predictors often rely only on the dispersion of retrieval scores and neglect the effects of document clustering. To address these challenges, we propose a novel clustering-baxsed post-retrieval QPP method. Specifically, we introduce three unsupervised predictors: Clustered Distinction, which measures query-specific separability of retrieved clusters; Clustered Query Drift, which estimates the deviation of top-ranked documents from query intent; and a hybrid approach combining both. By analyzing the clustering structure of retrieved documents, our method improves interpretability while eliminating the need for labeled data. We evaluate our approach on the large-scale MS MARCO Passage Ranking dataset. Experimental results demonstrate that our method significantly outperforms state-of-the-art score-baxsed QPP models. These findings highlight the potential of cluster-aware QPP for enhancing IR systems and reducing the impact of difficult queries.