Multi-Density Clustering Algorithm Based on Grid and Boundary

2010 ◽  
Author(s):  
Yazhou Wang ◽  
Wei Wang
Keyword(s):  
Clustering Algorithm ◽  
Density Clustering

Multi-Radius Density Clustering Algorithm Based on Outlier Factor

2014 ◽  
Vol 472 ◽  
pp. 427-431
Author(s):  
Zong Lin Ye ◽  
Hui Cao ◽  
Li Xin Jia ◽  
Yan Bin Zhang ◽  
Gang Quan Si
Keyword(s):  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Similar Process ◽  
The Core ◽  
Dbscan Algorithm ◽  
Proposed Model ◽  
Density Clustering ◽  
Relationship Of ◽  
Core Points ◽  
The Relationship

This paper proposes a novel multi-radius density clustering algorithm based on outlier factor. The algorithm first calculates the density-similar-neighbor-based outlier factor (DSNOF) for each point in the dataset according to the relationship of the density of the point and its neighbors, and then treats the point whose DSNOF is smaller than 1 as a core point. Second, the core points are used for clustering by the similar process of the density based spatial clustering application with noise (DBSCAN) to get some sub-clusters. Third, the proposed algorithm merges the obtained sub-clusters into some clusters. Finally, the points whose DSNOF are larger than 1 are assigned into these clusters. Experiments are performed on some real datasets of the UCI Machine Learning Repository and the experiments results verify that the effectiveness of the proposed model is higher than the DBSCAN algorithm and k-means algorithm and would not be affected by the parameter greatly.

A novel image segmentation method based on fast density clustering algorithm

2018 ◽  
Vol 73 ◽  
pp. 92-110 ◽  
Author(s):  
Jinyin Chen ◽  
Haibin Zheng ◽  
Xiang Lin ◽  
Yangyang Wu ◽  
Mengmeng Su
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Segmentation Method ◽  
Density Clustering

scholarly journals Scalable Varied Density Clustering Algorithm for Large Datasets

2010 ◽  
Vol 03 (06) ◽  
pp. 593-602 ◽  
Author(s):  
Ahmed Fahim ◽  
Abd-Elbadeeh Salem ◽  
Fawzy Torkey ◽  
Mohamed Ramadan ◽  
Gunter Saake
Keyword(s):  
Clustering Algorithm ◽  
Large Datasets ◽  
Density Clustering

scholarly journals Scalable Clustering with Supervised Linkage Methods

2021 ◽  
Author(s):  
James Anibal ◽  
Alexandre Day ◽  
Erol Bahadiroglu ◽  
Liam O'Neill ◽  
Long Phan ◽  
...  
Keyword(s):  
Single Cell ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Biomedical Sciences ◽  
New Approach ◽  
Scalable Clustering ◽  
Linkage Methods ◽  
Density Clustering ◽  
Cell Data ◽  
Different Levels

Data clustering plays a significant role in biomedical sciences, particularly in single-cell data analysis. Researchers use clustering algorithms to group individual cells into populations that can be evaluated across different levels of disease progression, drug response, and other clinical statuses. In many cases, multiple sets of clusters must be generated to assess varying levels of cluster specificity. For example, there are many subtypes of leukocytes (e.g. T cells), whose individual preponderance and phenotype must be assessed for statistical/functional significance. In this report, we introduce a novel hierarchical density clustering algorithm (HAL-x) that uses supervised linkage methods to build a cluster hierarchy on raw single-cell data. With this new approach, HAL-x can quickly predict multiple sets of labels for immense datasets, achieving a considerable improvement in computational efficiency on large datasets compared to existing methods. We also show that cell clusters generated by HAL-x yield near-perfect F1-scores when classifying different clinical statuses based on single-cell profiles. Our hierarchical density clustering algorithm achieves high accuracy in single cell classification in a scalable, tunable and rapid manner. We make HAL-x publicly available at: https://pypi.org/project/hal-x/

Planar Feature Extraction and Fitting Method Based on Density Clustering Algorithm

2018 ◽  
Author(s):  
Min Zhang ◽  
Minzhou Luo ◽  
Xiaobin Xu ◽  
Zhiying Tan ◽  
Hao Yang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
Fitting Method ◽  
Density Clustering ◽  
Planar Feature
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