一种加入类间因素的曲线聚类算法

针对目前的曲线聚类算法基于类内差异设计，造成不同类之间的曲线区分度不高的问题。在曲线拟合、曲线距离界定的基础上，构造新的目标函数，提出同时考虑类内和类间差异的曲线聚类算法。模拟结果显示，该曲线聚类能够提高聚类精度；针对NO₂污染物小时浓度的实例分析表明，该曲线聚类算法具有更好的类间区分度。

Curve clustering algorithms by adding the differences among clusters

With the improvement of accuracy and frequency of data collection, functional data has appeared. Curves’ clustering is a fundamental exploratory task in functional data analysis, and To sovave currently curves clustering algorithms available are based on the differences within each cluster, which has resulted in a low distinction among different curves. Therefore, on the base of curve fitting and curve distance, and with constructed objective function, curves clustering algorithms will be put forward with the consideration of cluster differences. Simulated results show that the curve cluster improves clustering accuracy. The example analysis of hourly NO₂ concentration (μg/m³) indicates that this kind of curves clustering algorithms has a better distinction among different clusters.