A Density-based Clustering Algorithm Using Adaptive Parameter K-Reverse Nearest Neighbor

2019 ◽  
Author(s):  
Pengyu Pei ◽  
Dong Zhang ◽  
Feng Guo
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Reverse Nearest Neighbor ◽  
Adaptive Parameter ◽  
Density Based Clustering

An improved OPTICS clustering algorithm for discovering clusters with uneven densities

2021 ◽  
Vol 25 (6) ◽  
pp. 1453-1471
Author(s):  
Chunhua Tang ◽  
Han Wang ◽  
Zhiwen Wang ◽  
Xiangkun Zeng ◽  
Huaran Yan ◽  
...  
Keyword(s):  
Time Complexity ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Substantial Improvement ◽  
Experimental Results ◽  
High Time ◽  
Parameter Setting ◽  
K Nearest Neighbor ◽  
Density Based Clustering

Most density-based clustering algorithms have the problems of difficult parameter setting, high time complexity, poor noise recognition, and weak clustering for datasets with uneven density. To solve these problems, this paper proposes FOP-OPTICS algorithm (Finding of the Ordering Peaks Based on OPTICS), which is a substantial improvement of OPTICS (Ordering Points To Identify the Clustering Structure). The proposed algorithm finds the demarcation point (DP) from the Augmented Cluster-Ordering generated by OPTICS and uses the reachability-distance of DP as the radius of neighborhood eps of its corresponding cluster. It overcomes the weakness of most algorithms in clustering datasets with uneven densities. By computing the distance of the k-nearest neighbor of each point, it reduces the time complexity of OPTICS; by calculating density-mutation points within the clusters, it can efficiently recognize noise. The experimental results show that FOP-OPTICS has the lowest time complexity, and outperforms other algorithms in parameter setting and noise recognition.

scholarly journals DeepDBSCAN: Deep Density-Based Clustering for Geo-Tagged Photos

2021 ◽  
Vol 10 (8) ◽  
pp. 548
Author(s):  
Jang-You Park ◽  
Dong-June Ryu ◽  
Kwang-Woo Nam ◽  
Insung Jang ◽  
Minseok Jang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Learning Technology ◽  
Neighbor Graph ◽  
Density Based Clustering ◽  
Real Objects ◽  
Nearest Neighbor Graph

Density-based clustering algorithms have been the most commonly used algorithms for discovering regions and points of interest in cities using global positioning system (GPS) information in geo-tagged photos. However, users sometimes find more specific areas of interest using real objects captured in pictures. Recent advances in deep learning technology make it possible to recognize these objects in photos. However, since deep learning detection is a very time-consuming task, simply combining deep learning detection with density-based clustering is very costly. In this paper, we propose a novel algorithm supporting deep content and density-based clustering, called deep density-based spatial clustering of applications with noise (DeepDBSCAN). DeepDBSCAN incorporates object detection by deep learning into the density clustering algorithm using the nearest neighbor graph technique. Additionally, this supports a graph-based reduction algorithm that reduces the number of deep detections. We performed experiments with pictures shared by users on Flickr and compared the performance of multiple algorithms to demonstrate the excellence of the proposed algorithm.

scholarly journals Smooth Splicing: A Robust SNN-Based Method for Clustering High-Dimensional Data

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
JingDong Tan ◽  
RuJing Wang
Keyword(s):  
Graph Theory ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Clustering Methods ◽  
Single Edge ◽  
Density Based Clustering ◽  
Measure Of Similarity

Sharing nearest neighbor (SNN) is a novel metric measure of similarity, and it can conquer two hardships: the low similarities between samples and the different densities of classes. At present, there are two popular SNN similarity based clustering methods: JP clustering and SNN density based clustering. Their clustering results highly rely on the weighting value of the single edge, and thus they are very vulnerable. Motivated by the idea of smooth splicing in computing geometry, the authors design a novel SNN similarity based clustering algorithm within the structure of graph theory. Since it inherits complementary intensity-smoothness principle, its generalizing ability surpasses those of the previously mentioned two methods. The experiments on text datasets show its effectiveness.

A novel clustering algorithm based on the natural reverse nearest neighbor structure

2019 ◽  
Vol 84 ◽  
pp. 1-16 ◽  
Author(s):  
Qi-Zhu Dai ◽  
Zhong-Yang Xiong ◽  
Jiang Xie ◽  
Xiao-Xia Wang ◽  
Yu-Fang Zhang ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Reverse Nearest Neighbor

scholarly journals Quantum algorithm for MMNG-based DBSCAN

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xuming Xie ◽  
Longzhen Duan ◽  
Taorong Qiu ◽  
Junru Li
Keyword(s):  
Domain Knowledge ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Local Density ◽  
Quantum Algorithm ◽  
Neighbor Graph ◽  
Dbscan Algorithm ◽  
Density Based Clustering ◽  
Input Parameters ◽  
Nearest Neighbor Graph

AbstractDBSCAN is a famous density-based clustering algorithm that can discover clusters with arbitrary shapes without the minimal requirements of domain knowledge to determine the input parameters. However, DBSCAN is not suitable for databases with different local-density clusters and is also a very time-consuming clustering algorithm. In this paper, we present a quantum mutual MinPts-nearest neighbor graph (MMNG)-based DBSCAN algorithm. The proposed algorithm performs better on databases with different local-density clusters. Furthermore, the proposed algorithm has a dramatic increase in speed compared to its classic counterpart.

DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification

2015 ◽  
pp. 125-138 ◽  
Author(s):  
I. V. Goncharenko
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Hierarchical Cluster ◽  
Vegetation Classification ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.

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