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计算机工程 ›› 2009, Vol. 35 ›› Issue (6): 210-212. doi: 10.3969/j.issn.1000-3428.2009.06.074

• 人工智能及识别技术 • 上一篇    下一篇

密度敏感的距离测度在特定图像聚类中的应用

吴毓龙,袁平波   

  1. (中国科学技术大学电子工程与信息科学系,合肥 230027)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-03-20 发布日期:2009-03-20

Application of Density-sensitive Distance Measure in Special Image Clustering

  1. WU Yu-long, YUAN Ping-bo
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-03-20 Published:2009-03-20

摘要: 根据聚类假设,提出一种新的基于图的半监督学习算法,称为密度敏感的半监督聚类。该算法引入一种密度敏感的距离测度,它能较好地反映聚类假设,并且充分挖掘了数据集中复杂的内在结构信息,同时与基于图的半监督学习方法相结合,使得算法在聚类性能上有了显著的提高。经过实验仿真进一步表明,该算法在特定图像应用上具有优越性。

关键词: 半监督聚类, 密度敏感, 聚类假设

Abstract: Based on clustering assumption, this paper proposes a novel semi-supervised learning algorithm based on graph, named Density- sensitive Semi-supervised Clustering(DS-SC). The approach introduces a density-sensitive distance measure which reflects the clustering assumption well and fully exploits the competitive inherent structure information among dataset, and combining it with a graph-based semi-supervised learning methods leads to prominent clustering performance enhance of DS-SC. The results demonstrate the superiority of DS-SC in the application of a specific image in the further simulation.

Key words: semi-supervised clustering, density-sensitive, clustering assumption

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