山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (1): 19-27.doi: 10.6040/j.issn.1672-3961.0.2021.301
• • 上一篇
孟银凤1,杨佳宇1,曹付元2
MENG Yinfeng1, YANG Jiayu1, CAO Fuyuan2
摘要: 针对函数型数据无穷维、低信噪比和动态时变等导致传统聚类方法性能降低、运行速度变慢和结果可解释性变差等问题,提出一种分裂转移式层次聚类(split transfer hierarchical clustering, STHC)算法,与传统层次聚类算法相比,具有能够发现内部层次关系和无需设定聚类个数的特点,选择适当的函数主成分个数重构样本,达到降低曲线噪声的作用。对重构样本进行分裂转移式层次聚类,根据内部准则选出最优聚类个数,并在仿真数据和真实数据上与其他几种函数型数据聚类方法进行试验比较。结果表明,本研究提出的STHC算法具有良好的性能表现和较低的运行时间,同时能够识别出函数型数据的多层次结构,提高聚类结果的可解释性。
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