Noises Cutting and Natural Neighbors Spectral Clustering Based on Coupling P System
Clustering analysis, a key step for many data mining problems, can be applied to various fields. However, no matter what kind of clustering method, noise points have always been an important factor affecting the clustering effect. In addition, in spectral clustering, the construction of affinity mat...
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doaj-4b029bc0b0884529ad8fe0ae40bf4e762021-03-01T00:03:10ZengMDPI AGProcesses2227-97172021-02-01943943910.3390/pr9030439Noises Cutting and Natural Neighbors Spectral Clustering Based on Coupling P SystemXiaoling Zhang0Xiyu Liu1Business School, Shandong Normal University, Jinan 250014, ChinaBusiness School, Shandong Normal University, Jinan 250014, ChinaClustering analysis, a key step for many data mining problems, can be applied to various fields. However, no matter what kind of clustering method, noise points have always been an important factor affecting the clustering effect. In addition, in spectral clustering, the construction of affinity matrix affects the formation of new samples, which in turn affects the final clustering results. Therefore, this study proposes a noise cutting and natural neighbors spectral clustering method based on coupling P system (<i>NCNNSC-CP</i>) to solve the above problems. The whole algorithm process is carried out in the coupled P system. We propose a natural neighbors searching method without parameters, which can quickly determine the natural neighbors and natural characteristic value of data points. Then, based on it, the critical density and reverse density are obtained, and noise identification and cutting are performed. The affinity matrix constructed using core natural neighbors greatly improve the similarity between data points. Experimental results on nine synthetic data sets and six UCI datasets demonstrate that the proposed algorithm is better than other comparison algorithms.https://www.mdpi.com/2227-9717/9/3/439natural neighborsnoisesspectral ClusteringP system |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaoling Zhang Xiyu Liu |
spellingShingle |
Xiaoling Zhang Xiyu Liu Noises Cutting and Natural Neighbors Spectral Clustering Based on Coupling P System Processes natural neighbors noises spectral Clustering P system |
author_facet |
Xiaoling Zhang Xiyu Liu |
author_sort |
Xiaoling Zhang |
title |
Noises Cutting and Natural Neighbors Spectral Clustering Based on Coupling P System |
title_short |
Noises Cutting and Natural Neighbors Spectral Clustering Based on Coupling P System |
title_full |
Noises Cutting and Natural Neighbors Spectral Clustering Based on Coupling P System |
title_fullStr |
Noises Cutting and Natural Neighbors Spectral Clustering Based on Coupling P System |
title_full_unstemmed |
Noises Cutting and Natural Neighbors Spectral Clustering Based on Coupling P System |
title_sort |
noises cutting and natural neighbors spectral clustering based on coupling p system |
publisher |
MDPI AG |
series |
Processes |
issn |
2227-9717 |
publishDate |
2021-02-01 |
description |
Clustering analysis, a key step for many data mining problems, can be applied to various fields. However, no matter what kind of clustering method, noise points have always been an important factor affecting the clustering effect. In addition, in spectral clustering, the construction of affinity matrix affects the formation of new samples, which in turn affects the final clustering results. Therefore, this study proposes a noise cutting and natural neighbors spectral clustering method based on coupling P system (<i>NCNNSC-CP</i>) to solve the above problems. The whole algorithm process is carried out in the coupled P system. We propose a natural neighbors searching method without parameters, which can quickly determine the natural neighbors and natural characteristic value of data points. Then, based on it, the critical density and reverse density are obtained, and noise identification and cutting are performed. The affinity matrix constructed using core natural neighbors greatly improve the similarity between data points. Experimental results on nine synthetic data sets and six UCI datasets demonstrate that the proposed algorithm is better than other comparison algorithms. |
topic |
natural neighbors noises spectral Clustering P system |
url |
https://www.mdpi.com/2227-9717/9/3/439 |
work_keys_str_mv |
AT xiaolingzhang noisescuttingandnaturalneighborsspectralclusteringbasedoncouplingpsystem AT xiyuliu noisescuttingandnaturalneighborsspectralclusteringbasedoncouplingpsystem |
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1724247250631131136 |