A hierarchical clustering algorithm based on noise removal

Noise is irrelevant or meaningless data and hinders most types of data analysis. The existing clustering algorithms seldom take the noise points into consideration and cannot detect arbitrary-shaped clusters. This paper presents a Hierarchical Clustering algorithm Based on Noise Removal (HCBNR). It...

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Bibliographic Details
Published in:International journal of machine learning and cybernetics Vol. 10; no. 7; pp. 1591 - 1602
Main Authors: Cheng, Dongdong, Zhu, Qingsheng, Huang, Jinlong, Wu, Quanwang, Yang, Lijun
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2019
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Cluster analysis
Clustering
Complex Systems
Computational Intelligence
Control
Data analysis
Datasets
Engineering
Mechatronics
Methods
Modularity
Noise
Original Article
Pattern Recognition
Robotics
Synthetic data
Systems Biology
Hierarchical clustering
Noise removal
Natural neighbor
ISSN:1868-8071, 1868-808X
Online Access:Get full text
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Summary:Noise is irrelevant or meaningless data and hinders most types of data analysis. The existing clustering algorithms seldom take the noise points into consideration and cannot detect arbitrary-shaped clusters. This paper presents a Hierarchical Clustering algorithm Based on Noise Removal (HCBNR). It is robust against noise points and good at discovering clusters with arbitrary shapes. In this work, natural neighbor-based density is applied to remove noise points in a data set firstly. Then we construct a saturated neighbor graph on the rest points, and a novel modularity-based graph partitioning algorithm is used to divide the graph into small clusters. Finally, the small clusters are repeatedly merged according to a novel similarity metric between clusters until the desired cluster number is obtained. The experimental results on synthetic data sets and real data sets show that our method can accurately identify noise points and obtain better clustering results than existing clustering algorithms when discovering arbitrary-shaped clusters.
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ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-018-0836-3