The Improvement on Self-Adaption Select Cluster Centers Based on Fast Search and Find of Density Peaks Clustering

In order to solve the problem of manual selection of cluster centers in density peaks clustering algorithm, an automatic selection algorithm of cluster centers was proposed in this paper, which can calculate the change rate and difference for each data. Firstly, the local density p and the high dens...

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Bibliographic Details
Published in:2020 16th International Conference on Computational Intelligence and Security (CIS) pp. 234 - 237
Main Authors: Du, Hui, Ni, Yiyang
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2020
Subjects:
change rate
Clustering algorithms
component
Computational intelligence
density peaks clustering algorithm
difference
Manuals
Security
self-adaption
Online Access:Get full text
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Summary:In order to solve the problem of manual selection of cluster centers in density peaks clustering algorithm, an automatic selection algorithm of cluster centers was proposed in this paper, which can calculate the change rate and difference for each data. Firstly, the local density p and the high density nearest distance δ of each data point were multiplied and sorted to calculate the difference value A between two adjacent data points, where A is a group of finite sequences from big to small, and the ratio of each item in the sequence to its next term is θ. Through the threshold range of θ and A, the cluster centers can be selected adaptively, and the number of clusters can be determined automatically. Experiment results have shown that the algorithm is suitable for non-convex data with good clustering effect.
DOI:10.1109/CIS52066.2020.00057