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OALib Journal期刊
ISSN: 2333-9721
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基于局部和全局信息的正则化迭代聚类
Keywords: 凸形,谱聚类,局部正则化,全局正则化,迭代
Abstract:
聚类是一种高效的数据分析方法,经典的k-means算法只适用于类簇为凸形的数据集,谱聚类算法虽然避免了k-means的一些缺点,但相似度中的参数设置问题以及较高的计算、存储复杂度对聚类有所限制.基于局部和全局信息的正则化迭代聚类,先取部分数据作为一个整体聚类,然后逐渐加入少量数据进行迭代求解.该方法继承传统谱聚类的优点,充分利用局部正则化和全局正则化信息,通过迭代方式求解使较大规模数据聚类成为可能.通过实验对比结果显示,该算法有良好的聚类效果.
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