KNN-DBSCAN: Using k-nearest neighbor information for parameter-free density based clustering
2017 International Conference on Intelligent Computing …, 2017•ieeexplore.ieee.org
Density based clustering is adopted in situations where clusters of arbitrary shape exist.
DBSCAN is a popular density concept but suffers from the drawback of dependence on user-
defined parameters like many other density based methods. In order to utilize the potential of
this clustering method we propose a combination method. The information of k-nearest
neighbors is used with DBSCAN to achieve a parameter-free clustering technique. The
parameters are set according to information of the data as it gets accumulated in a cluster …
DBSCAN is a popular density concept but suffers from the drawback of dependence on user-
defined parameters like many other density based methods. In order to utilize the potential of
this clustering method we propose a combination method. The information of k-nearest
neighbors is used with DBSCAN to achieve a parameter-free clustering technique. The
parameters are set according to information of the data as it gets accumulated in a cluster …
Density based clustering is adopted in situations where clusters of arbitrary shape exist. DBSCAN is a popular density concept but suffers from the drawback of dependence on user-defined parameters like many other density based methods. In order to utilize the potential of this clustering method we propose a combination method. The information of k-nearest neighbors is used with DBSCAN to achieve a parameter-free clustering technique. The parameters are set according to information of the data as it gets accumulated in a cluster structure.
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