A Data-Driven Parameter Adaptive Clustering Algorithm Based on Density Peak

Clustering is an important unsupervised machine learning method which can efficiently partition points without training data set. However, most of the existing clustering algorithms need to set parameters artificially, and the results of clustering are much influenced by these parameters, so optimiz...

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Vydané v:Complexity (New York, N.Y.) Ročník 2018; číslo 2018; s. 1 - 14
Hlavní autori: Du, Tao, Wang, Qin, Qu, Shouning
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
John Wiley & Sons, Inc
Wiley
Predmet:
Adaptive algorithms
Algorithms
Clustering
Complexity
Datasets
Density
Efficiency
Information science
Knowledge discovery
Machine learning
Neighborhoods
Parameters
Pattern recognition
Principal components analysis
Specific gravity
ISSN:1076-2787, 1099-0526
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Shrnutí:Clustering is an important unsupervised machine learning method which can efficiently partition points without training data set. However, most of the existing clustering algorithms need to set parameters artificially, and the results of clustering are much influenced by these parameters, so optimizing clustering parameters is a key factor of improving clustering performance. In this paper, we propose a parameter adaptive clustering algorithm DDPA-DP which is based on density-peak algorithm. In DDPA-DP, all parameters can be adaptively adjusted based on the data-driven thought, and then the accuracy of clustering is highly improved, and the time complexity is not increased obviously. To prove the performance of DDPA-DP, a series of experiments are designed with some artificial data sets and a real application data set, and the clustering results of DDPA-DP are compared with some typical algorithms by these experiments. Based on these results, the accuracy of DDPA-DP has obvious advantage of all, and its time complexity is close to classical DP-Clust.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1076-2787
1099-0526
DOI:10.1155/2018/5232543