An Adaptive Density Peaks Clustering Method With Fisher Linear Discriminant

An Adaptive Density Peaks Clustering Method With Fisher Linear Discriminant

Clustering is one of the most important topics in data mining and machine learning. The density peaks clustering (DPC) algorithm is a well-known density-based clustering method that can efficiently and effectively deal with non-spherical clusters. However, the computational methods of the local dens...

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Bibliographic Details
Main Authors: Lin Sun, Ruonan Liu, Jiucheng Xu, Shiguang Zhang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Density peaks clustering
Kernel density estimation function
density estimation entropy
Fisher linear discriminant
reduction
Online Access:https://ieeexplore.ieee.org/document/8721690/

Internet

https://ieeexplore.ieee.org/document/8721690/

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