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Hierarchical clustering driven by cognitive features
Ist Teil von
Science China. Information sciences, 2014, Vol.57 (1), p.107-120
Ort / Verlag
Heidelberg: Science China Press
Erscheinungsjahr
2014
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
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 diferent shapes and densities. Some synthetic and real data sets are employed to exhibit the superiority of the new clustering algorithm.