Hierarchical clustering driven by cognitive features

作者：Li ChunZhong^*; Xu ZongBen; Qiao Chen; Luo Tao

来源：Science China Information Sciences, 2014, 57(1): 012109.

DOI：10.1007/s11432-013-4858-x

• clustering
• direction consistent
• structural nearest neighborhood
• affecting field
• manifold

For data sets of arbitrary shapes and densities, the existing clusterings have much space to be improved to obtain better results. In this paper, clustering is considered as a cognitive problem, and cognitive features are of vital importance to clustering. In combination with psychological experiment, we propose three cognitive features of clustering and model them as a flexible similarity measurement. Meanwhile a new clustering framework is put forward to integrate the cognitive features by employing the similarity measurement. The two attractive advantages are its low complexity and fitness for various types of data sets, such as data sets of different shapes and densities. Some synthetic and real data sets are employed to exhibit the superiority of the new clustering algorithm.

• 出版日期2014-1
• 单位安徽大学; 西安交通大学