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δ ‐Open set clustering—A new topological clustering method
Author(s) -
Wang Shuliang,
Li Qi,
Yuan Hanning,
Li Deren,
Geng Jing,
Zhao Chuanfeng,
Lei Yimeng,
Liu Chuanlu,
Liu Chengfei
Publication year - 2018
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1262
Subject(s) - cluster analysis , correlation clustering , cure data clustering algorithm , computer science , clustering high dimensional data , data mining , canopy clustering algorithm , data stream clustering , single linkage clustering , artificial intelligence , pattern recognition (psychology) , dbscan , consensus clustering , data set , fuzzy clustering , set (abstract data type) , constrained clustering , determining the number of clusters in a data set , programming language
Clustering is an unsupervised learning method widely used for identifying the inherent data structure and applied to various fields such as data mining, patter recognition, machine learning, and others. A new topological clustering method called δ ‐open set clustering is proposed in this study. The key idea of this method is to determine δ ‐open sets in data, for which each δ ‐open set represents one specific category of data. It is shown that this method has robust performance even for complex data set. It can classify the complex type of data sets coming with diverse shapes, recognize noise and deal with data set of high dimensionality. This method is effective even when the distribution of data is unbalanced. In the clustering process, one requires a single input parameter, namely the value of δ . A face identification experiment on the Olivetti Face Database indicates that this method performs much more reliably than the peak clustering method. We also provide another improved δ ‐open set clustering that makes δ ‐open set clustering capable of handling clusters with extreme density difference. This article is categorized under: Technologies > Structure Discovery and Clustering Algorithmic Development > Structure Discovery