Density Peak Clustering algorithm using knowledge learning-based fruit fly optimization

2018 ◽  
Vol 40 (3) ◽  
pp. 1-10
Author(s):  
Ruihong Zhou ◽  
Qiaoming Liu ◽  
Xuming Han ◽  
Limin Wang
Keyword(s):  
Clustering Algorithm ◽  
Fruit Fly ◽  
Density Peak ◽  
Fruit Fly Optimization ◽  
Density Peak Clustering ◽  
Knowledge Learning

scholarly journals Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Wei Li ◽  
Ranran Deng ◽  
Yingjie Zhang ◽  
Zhaoyun Sun ◽  
Xueli Hao ◽  
...  
Keyword(s):  
Crack Detection ◽  
Clustering Algorithm ◽  
Asphalt Pavement ◽  
Fruit Fly ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Density Peak ◽  
3D Data ◽  
Density Peak Clustering ◽  
Pavement Crack Detection

Complex pavement texture and noise impede the effectiveness of existing 3D pavement crack detection methods. To improve pavement crack detection accuracy, we propose a 3D asphalt pavement crack detection algorithm based on fruit fly optimisation density peak clustering (FO-DPC). Firstly, the 3D data of asphalt pavement are collected, and a 3D image acquisition system is built using Gocator3100 series binocular intelligent sensors. Then, the fruit fly optimisation algorithm is adopted to improve the density peak clustering algorithm. Clustering analysis that can accurately detect cracks is performed on the height characteristics of the 3D data of the asphalt pavement. Finally, the clustering results are projected onto a 2D space and compared with the results of other 2D crack detection methods. Following this comparison, it is established that the proposed algorithm outperforms existing methods in detecting asphalt pavement cracks.

scholarly journals A Fast Density Peak Clustering Algorithm Optimized by Uncertain Number Neighbors for Breast MR Image

2019 ◽  
Vol 1229 ◽  
pp. 012024 ◽  
Author(s):  
Fan Hong ◽  
Yang Jing ◽  
Hou Cun-cun ◽  
Zhang Ke-zhen ◽  
Yao Ruo-xia
Keyword(s):  
Clustering Algorithm ◽  
Mr Image ◽  
Density Peak ◽  
Breast Mr ◽  
Density Peak Clustering

scholarly journals Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering

2019 ◽  
Vol 33 ◽  
pp. 4755-4762 ◽  
Author(s):  
Xiaoyu Qin ◽  
Kai Ming Ting ◽  
Ye Zhu ◽  
Vincent CS Lee
Keyword(s):  
Clustering Algorithm ◽  
Distance Measure ◽  
Nearest Neighbour ◽  
Density Peak ◽  
Density Based Clustering ◽  
New Type ◽  
Density Peak Clustering ◽  
The Impact ◽  
First Time ◽  
Tree Method

A recent proposal of data dependent similarity called Isolation Kernel/Similarity has enabled SVM to produce better classification accuracy. We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead. We formally prove the characteristic of Isolation Similarity with the use of the proposed method. The impact of Isolation Similarity on densitybased clustering is studied here. We show for the first time that the clustering performance of the classic density-based clustering algorithm DBSCAN can be significantly uplifted to surpass that of the recent density-peak clustering algorithm DP. This is achieved by simply replacing the distance measure with the proposed nearest-neighbour-induced Isolation Similarity in DBSCAN, leaving the rest of the procedure unchanged. A new type of clusters called mass-connected clusters is formally defined. We show that DBSCAN, which detects density-connected clusters, becomes one which detects mass-connected clusters, when the distance measure is replaced with the proposed similarity. We also provide the condition under which mass-connected clusters can be detected, while density-connected clusters cannot.

A privacy‐preserving density peak clustering algorithm in cloud computing

2020 ◽  
Vol 32 (11) ◽  
Author(s):  
Liping Sun ◽  
Shang Ci ◽  
Xiaoqing Liu ◽  
Xiaoyao Zheng ◽  
Qingying Yu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Clustering Algorithm ◽  
Privacy Preserving ◽  
Density Peak ◽  
Density Peak Clustering

scholarly journals Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1168
Author(s):  
Jun-Lin Lin ◽  
Jen-Chieh Kuo ◽  
Hsing-Wang Chuang
Keyword(s):  
Clustering Algorithm ◽  
Academic Community ◽  
Performance Study ◽  
Potential Density ◽  
Cluster Assignment ◽  
Density Peak ◽  
Single Linkage ◽  
Density Peaks ◽  
Assignment Strategy ◽  
Density Peak Clustering

Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its nearest higher-density point. One problem with DPC is the determination of the density peaks, where poor selection of the density peaks could yield poor clustering results. Another problem with DPC is its cluster assignment strategy, which often makes incorrect cluster assignments for data points that are far from their nearest higher-density points. This study modifies DPC and proposes a new clustering algorithm to resolve the above problems. The proposed algorithm uses the radius of the neighborhood to automatically select a set of the likely density peaks, which are far from their nearest higher-density points. Using the potential density peaks as the density peaks, it then applies DPC to yield the preliminary clustering results. Finally, it uses single-linkage clustering on the preliminary clustering results to reduce the number of clusters, if necessary. The proposed algorithm avoids the cluster assignment problem in DPC because the cluster assignments for the potential density peaks are based on single-linkage clustering, not based on DPC. Our performance study shows that the proposed algorithm outperforms DPC for datasets with irregularly shaped clusters.

scholarly journals VDPC: Variational Density Peak Clustering Algorithm

2021 ◽  
Author(s):  
Yizhang Wang ◽  
Di Wang ◽  
You Zhou ◽  
Chai Quek ◽  
Xiaofeng Zhang
Keyword(s):  
Clustering Algorithm ◽  
Cluster Formation ◽  
Clustering Algorithms ◽  
Data Distribution ◽  
Distribution Patterns ◽  
Clustering Methods ◽  
Density Peak ◽  
Global Parameter ◽  
Density Peak Clustering ◽  
Parameter Values

<div>Clustering is an important unsupervised knowledge acquisition method, which divides the unlabeled data into different groups \cite{atilgan2021efficient,d2021automatic}. Different clustering algorithms make different assumptions on the cluster formation, thus, most clustering algorithms are able to well handle at least one particular type of data distribution but may not well handle the other types of distributions. For example, K-means identifies convex clusters well \cite{bai2017fast}, and DBSCAN is able to find clusters with similar densities \cite{DBSCAN}. </div><div>Therefore, most clustering methods may not work well on data distribution patterns that are different from the assumptions being made and on a mixture of different distribution patterns. Taking DBSCAN as an example, it is sensitive to the loosely connected points between dense natural clusters as illustrated in Figure~\ref{figconnect}. The density of the connected points shown in Figure~\ref{figconnect} is different from the natural clusters on both ends, however, DBSCAN with fixed global parameter values may wrongly assign these connected points and consider all the data points in Figure~\ref{figconnect} as one big cluster.</div>

scholarly journals Optimization of Density Peak Clustering Algorithm Based on OpenMP

2018 ◽  
Vol 13 (3) ◽  
pp. 168-179
Author(s):  
Anbo Qiu ◽  
◽  
Zhuowei Wang
Keyword(s):  
Clustering Algorithm ◽  
Density Peak ◽  
Density Peak Clustering

scholarly journals Quality Based Clustering of Node using Fuzzy-Fruit Fly Optimization for Cluster Head and Gateway Selection in Healthcare Application

2021 ◽  
Author(s):  
P Rahul ◽  
B Kaarthick
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
Loss Rate ◽  
Ad Hoc ◽  
Cluster Head ◽  
Fruit Fly ◽  
Fruit Fly Optimization ◽  
Gateway Selection ◽  
Mobile Ad Hoc ◽  
Hoc Networks

Abstract Big data recently has gained tremendous importance in the way information is being disseminated. Transaction based data, unstructured data streaming to and fro from social media, increasing amounts of sensor and machine-to-machine data and many such examples rely on big data in conjunction with cloud computing. It is desirable to create wireless networks on-the-fly as per the demand or a given situation. In such a scenario reliable transmission of big data over mobile Ad-Hoc networks plays a key role in military healthcare applications. Limitations like congestion, Delay, Energy Consumption and Packet Loss Rate constraint pose a challenge for such systems. The most essential problem of Hybrid Mobile Ad-hoc Networks (H-MANET) is to select a suitable and secure path that balances the load through the Internet gateways. Also, the selection of gateway and overload through the network may cause packet losses and Delay (DL). Therefore, load-balancing between different gateways is required for achieving better performance. As a result, steady load balancing technique was employed that selects the gateways based on the Fuzzy Logic (FL) system and enhances the network efficiency. However, the Energy Consumption (EC) was high since gateways were selected directly from the number of nodes. Hence in this article, a novel Node Quality-based Clustering Algorithm (NQCA) using Fuzzy-Genetic for Cluster Head and Gateway Selection (FGCHGS) is proposed. In this algorithm, NQCA is performed based on the Improved Weighted Clustering Algorithm (IWCA). The NQCA algorithm separates the total network into number of clusters and the Cluster Head (CH) for each cluster is elected on the basis of the node priority, transmission range and node neighborhood fidelity. Moreover, the clustering quality is estimated according to the different parameters like node degree, EC, DL, etc, which are also utilized for estimating the combined weight value by using the FL system. Then, the combined weight values are optimized by using Genetic Algorithm (GA) to pick the most optimal weight value that selects both optimal CH and gateway. Conversely, the convergence time of GA and the error due to parameter tuning during optimization are high. Hence, a NQCA using Fuzzy-Fruit Fly optimization for Cluster Head and Gateway Selection (FFFCHGS) is proposed. In this algorithm, improved Fruit Fly (FF) algorithm is proposed instead of GA to select the most optimal CH and gateway. Finally, a performance effectiveness of the FFFCHGS algorithm is evaluated through the simulation outcomes in terms of EC, Packet Loss Rate (PLR), etc.

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