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.

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.

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.

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.

GNN-DBSCAN: A new density-based algorithm using grid and the nearest neighbor

2021 ◽  
pp. 1-13
Author(s):  
Li Yihong ◽  
Wang Yunpeng ◽  
Li Tao ◽  
Lan Xiaolong ◽  
Song Han
Keyword(s):  
Mutual Information ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Adjusted Rand Index ◽  
K Nearest Neighbors ◽  
Normalized Mutual Information ◽  
Core Samples ◽  
Real World Datasets

DBSCAN (density-based spatial clustering of applications with noise) is one of the most widely used density-based clustering algorithms, which can find arbitrary shapes of clusters, determine the number of clusters, and identify noise samples automatically. However, the performance of DBSCAN is significantly limited as it is quite sensitive to the parameters of eps and MinPts. Eps represents the eps-neighborhood and MinPts stands for a minimum number of points. Additionally, a dataset with large variations in densities will probably trap the DBSCAN because its parameters are fixed. In order to overcome these limitations, we propose a new density-clustering algorithm called GNN-DBSCAN which uses an adaptive Grid to divide the dataset and defines local core samples by using the Nearest Neighbor. With the help of grid, the dataset space will be divided into a finite number of cells. After that, the nearest neighbor lying in every filled cell and adjacent filled cells are defined as the local core samples. Then, GNN-DBSCAN obtains global core samples by enhancing and screening local core samples. In this way, our algorithm can identify higher-quality core samples than DBSCAN. Lastly, give these global core samples and use dynamic radius based on k-nearest neighbors to cluster the datasets. Dynamic radius can overcome the problems of DBSCAN caused by its fixed parameter eps. Therefore, our method can perform better on dataset with large variations in densities. Experiments on synthetic and real-world datasets were conducted. The results indicate that the average Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Adjusted Mutual Information (AMI) and V-measure of our proposed algorithm outperform the existing algorithm DBSCAN, DPC, ADBSCAN, and HDBSCAN.

Towards ultra-latency using deep learning in 5G Network Slicing applying Approximate k-Nearest Neighbor Graph Construction

2021 ◽  
Author(s):  
Rohit Kumar Gupta ◽  
Praduman Pannu ◽  
Rajiv Misra
Keyword(s):  
Deep Learning ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Network Slicing ◽  
Neighbor Graph ◽  
Novel Approach ◽  
5G Network ◽  
Remote Healthcare ◽  
Nearest Neighbor Graph ◽  
Deep Learning Neural Network

Abstract The 5G Network Slicing with SDN and NFV have expended to support new-verticals such as intelligent transport, industrial automation, remote healthcare. Network slice is intended as parameter configurations and a collection of logical network functions to support particular service requirements. The network slicing resource allocation and prediction in 5G networks is carried out using network Key Performance Indicators (KPIs) from the connection request made by the devices on joining the network. We explore derived features as the network non-KPI parameters using the k-Nearest Neighbor (kNN) graph construction. In this paper, we use kNN graph construction algorithms to augment the dataset with triangle count and cluster coecient properties for ecient and reliable network slice. We used deep learning neural network model to simulate our results with KPIs and KPIs with non-KPI parameters. Our novel approach found that at k=3 and k=4 of the kNN graph construction gives better results and overall accuracy is imroved around 29%.

scholarly journals An Enhanced Spectral Clustering Algorithm with S-Distance

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
Krishna Kumar Sharma ◽  
Ayan Seal ◽  
Enrique Herrera-Viedma ◽  
Ondrej Krejcar
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Rank Test ◽  
Customer Churn ◽  
Signed Rank ◽  
Signed Rank Test ◽  
Spectral Clustering Algorithm ◽  
Industrial Databases

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape.

Distance Density Based Clustering Algorithm in Wireless Sensor Network

2011 ◽  
Vol 291-294 ◽  
pp. 344-348
Author(s):  
Lin Lin ◽  
Shu Yan ◽  
Yi Nian
Keyword(s):  
Clustering Algorithm ◽  
Distribution Density ◽  
Simulation Experiment ◽  
Clustering Algorithms ◽  
Wireless Sensor ◽  
Energy Usage ◽  
Cluster Heads ◽  
Hierarchical Topology ◽  
Energy Factors ◽  
Density Based Clustering

The hierarchical topology of wireless sensor networks can effectively reduce the consumption in communication. Clustering algorithm is the foundation to realize herarchical structure, so it has been extensive researched. On the basis of Leach algorithm, a distance density based clustering algorithm (DDBC) is proposed, considering synthetically the distribution density of around nodes and the remaining energy factors of the node to dynamically banlance energy usage of nodes when selecting cluster heads. We analyzed the performance of DDBC through compared with the existing other clustering algorithms in simulation experiment. Results show that the proposed method can generare stable quantity cluster heads and banlance the energy load effectively.

A Novel clustering method based on hybrid K-nearest-neighbor graph

2018 ◽  
Vol 74 ◽  
pp. 1-14 ◽  
Author(s):  
Yikun Qin ◽  
Zhu Liang Yu ◽  
Chang-Dong Wang ◽  
Zhenghui Gu ◽  
Yuanqing Li
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Clustering Method ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph
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