计算机科学 ›› 2016, Vol. 43 ›› Issue (12): 209-212.doi: 10.11896/j.issn.1002-137X.2016.12.038
周海松,黄德才
ZHOU Hai-song and HUANG De-cai
摘要: 谱聚类是一种新兴的聚类算法,数据点间的相似度定义对其聚类效果起着至关重要的作用。传统的谱聚类算法通常利用高斯核函数作为相似度函数,但是对于多密度的数据往往不能取得良好的效果。在定义新的相似度函数的基础上,提出了一种密度自适应的半监督聚类算法。该算法结合半监督聚类的成对约束理论,利用先验信息对样本点之间的相似度进行自适应调整,提高了聚类的精度。该算法在人工数据集和真实数据集上的仿真实验都取得了良好的效果。
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