发表机构
Rzeszów University of Technology（热舒夫工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
大数据时代聚类算法面临挑战，K-SCAN算法先经随机小批量K-Means提取加权微簇集，再做基于密度的结构分析，具线性计算复杂度，速度比BIRCH算法快超3倍，能精确识别非线性流形，保持结构稳定性，但有过度平滑等局限。
AI 中文摘要
在大数据时代，聚类算法的可扩展性是关键挑战。传统基于密度的方法（如DBSCAN）对噪声有鲁棒性且能检测非线性簇，但二次时间复杂度限制其应用；划分算法（如K-Means）线性复杂度但对异常值敏感。本文提出K-SCAN，一种优化权衡的新型混合算法。它先通过随机小批量K-Means进行向量量化提取加权微簇集，再进行基于密度的结构分析。对高达10^6样本数据集的实证评估证实其线性计算复杂度，比层次BIRCH算法加速超3倍，能精确识别非线性流形且保持结构稳定性，不过存在过度平滑及难以分离局部密度高度异质簇的问题。
英文摘要
In the Big Data era, the scalability of clustering algorithms constitutes a key challenge. Traditional density-based methods (e.g., DBSCAN) offer robustness to noise and the ability to detect non-linear clusters, yet their quadratic time complexity $O(N^2)$ drastically limits their applicability. Conversely, partitional algorithms (e.g., K-Means), with their linear complexity $O(N)$, impose sphericity on the resulting groups and fail in the presence of outliers. This paper presents K-SCAN -- a novel hybrid algorithm that optimizes this trade-off. The method integrates preliminary vector quantization (stochastic Mini-Batch K-Means) to extract a reduced set of weighted micro-clusters, followed by a subsequent density-based structural analysis. Empirical evaluation on datasets of up to $10^6$ samples confirms the linear computational complexity of the proposed solution. K-SCAN achieves more than a 3-fold speed-up over the hierarchical BIRCH algorithm, avoiding the costly management of tree-based structures. The method precisely identifies non-linear manifolds while maintaining structural stability (Adjusted Rand Index > 0.99), even with noise levels reaching 55\% of the data volume. The main limitation of the proposed algorithm, which could not be fully eliminated in the present study, remains its susceptibility to over-smoothing and its difficulty in separating clusters with highly heterogeneous local density. In complex visual spaces, this can lead to the loss of the finest topological details.
