提出一种高效单遍流式聚类算法，可处理非球形数据并动态更新聚类信息。
Single-pass Possibilistic Clustering with Damped Window Footprints
- 基于阻尼窗口实现聚类足印的闭式更新，支持任意大小窗口。
- 采用多假设追踪中的协方差合并方法，提升聚类中心与协方差估计精度。
- 适用于实时传感器数据或网络流量分析，适合快速部署新数据集。
流式聚类在大数据时代变得极为重要，例如在网络流量分析或持续运行的传感器数据处理中。可能性模型相较于现有方法具有独特优势，尤其引入了“模糊化参数”以控制典型性随距离簇中心增加而衰减的速度。本文提出一种单遍可能性聚类（SPC）算法，具备高效且易于应用到新数据集的特点。SPC的关键贡献包括：能够建模非球形簇、在任意大小的阻尼窗口上实现足印的闭式更新，以及利用多假设追踪领域的协方差联合方法合并两个簇均值与协方差估计。SPC在聚类纯度和归一化互信息指标上，相较于其他五种流式聚类算法进行了验证。
原文摘要 · Abstract (English)
Streaming clustering is a domain that has become extremely relevant in the age of big data, such as in network traffic analysis or in processing continuously-running sensor data. Furthermore, possibilistic models offer unique benefits over approaches from the literature, especially with the introduction of a "fuzzifier" parameter that controls how quickly typicality degrades as one gets further from cluster centers. We propose a single-pass possibilistic clustering (SPC) algorithm that is effective and easy to apply to new datasets. Key contributions of SPC include the ability to model non-spherical clusters, closed-form footprint updates over arbitrarily sized damped windows, and the employment of covariance union from the multiple hypothesis tracking literature to merge two cluster mean and covariance estimates. SPC is validated against five other streaming clustering algorithm on the basis of cluster purity and normalized mutual information.
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