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Effective Utilization of Shared Nearest Node for Message Diffusion in Social Network Using Dbscan

P. Apoorva, S. Akshay, R. Priyanka, N. Nayana
2018 International Journal of Engineering & Technology  
Density-based clustering is a relevant method we have used to trace shared nearest neighbor node.  ...  Also, we provide security for the data that is being diffused by implementing the RSA security algorithm and providing the security key along with the information and hence the group of people who are  ...  LeventErtoz offered the definitions for density and the suitable measure for similarity to analyze the data in high dimensionality.  ... 
doi:10.14419/ijet.v7i4.36.24535 fatcat:vn777epmvnddfestcsqrj3xdae

Multidimensional Discrete Big Data Clustering Algorithm Based on Dynamic Grid

Xiaolei Li, Mohammad Farukh Hashmi
2022 Wireless Communications and Mobile Computing  
The principal component analysis was used to reduce the dimension of data.  ...  Traditionally, the data clustering algorithm is lack of comprehensive performance, leading to low clustering purity and long clustering time.  ...  The high-dimensional data clustering technology refers to the clustering technology in high dimensional data space.  ... 
doi:10.1155/2022/4663816 fatcat:5pljppvpv5arhd7e64kyqc7x7e

A Novel Fault Location Method for Power Cables Based on an Unsupervised Learning Algorithm

Mingzhen Li, Jialong Bu, Yupeng Song, Zhongyi Pu, Yuli Wang, Cheng Xie
2021 Energies  
The main improvement is that high-dimensional data can be directly used for the clustering, making the method more effective and accurate.  ...  and the stability, using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN); (3) search for the characteristic cluster point(s) of the two branch clusters with the smallest  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/en14041164 fatcat:fz6thsvdg5g6lodgurjazfjjuy

Scalable Clustering with Supervised Linkage Methods [article]

James Anibal, Alexandre Day, Erol Bahadiroglu, Liam O'Neill, Long Phan, Alec Peltekian, Amir Erez, Mariana Kaplan, Gregoire Altan-Bonnet, Pankaj Mehta
2021 bioRxiv   pre-print
Our hierarchical density clustering algorithm achieves high accuracy in single cell classification in a scalable, tunable and rapid manner.  ...  Data clustering plays a significant role in biomedical sciences, particularly in single-cell data analysis.  ...  This project was initiated with a seed grant to GA-B and PM from the Gordon and Betty Moore foundation and Research Corporation through the Scialog program.  ... 
doi:10.1101/2021.08.01.454697 fatcat:ahyo7wczqrb2nkqfsn3qebjms4

Clustering Algorithms For High Dimensional Data – A Survey Of Issues And Existing Approaches

B.Hari Babu, N.Subash Chandra, T. Venu Gopal
2013 International Journal of Computer Science and Informatics  
The process of grouping into high dimensional data into clusters is not accurate and perhaps not up to the level of expectation when the dimension of the dataset is high.  ...  With the advent growth of high dimensional data such as microarray gene expression data, and grouping high dimensional data into clusters will encounter the similarity between the objects in the full dimensional  ...  ANALYSIS OF HIGH DIMENSIONAL DATA FOR CLUSTERING The rapid growth in various new application domains, like bioinformatics and e-commerce, reflects the need for analyzing high dimensional data.  ... 
doi:10.47893/ijcsi.2013.1108 fatcat:ortfep7hw5a6rhqrhgg7cfrbxm

Analysis of Abnormal Flight and Controllers Data Based on DBSCAN Method

Chen Zeng, Rundong Wang, Qinghai Zuo
2022 Security and Communication Networks  
hazards, this paper proposes a DBSCAN (density-based spatial clustering of applications with noise) clustering analysis method for aircraft airborne and controllers data outlier detection to evaluate  ...  The results show that the DBSCAN clustering anomaly data detection method based on density is fast and accurate in detecting the continuous parameters recorded in the flight process, and the display results  ...  data through accurate and fast data analysis methods [2] .  ... 
doi:10.1155/2022/7474270 doaj:ddeb492f091e44f9a27740d658ed18dc fatcat:pkmoobjj7ranjcbnbk2tr53xpy

High-dimensional Clustering for Incomplete Mixed Dataset Using Artificial Intelligence

Meishan Li, Xiaofeng Li, Jing Li
2020 IEEE Access  
In order to address the problem that high energy consumption, high memory usage and low clustering effect in traditional data set high-dimensional clustering algorithms, we propose the highdimensional  ...  Last, we realize the design of high-dimensional clustering method for incomplete mixed data set in accordance with the stronger relevance in the mixed data sets.  ...  get the conclusion according to probability density of measure: As the structures we found in the image data set do not meet the traditional clustering concept, we further propose a rapid method for high-dimensional  ... 
doi:10.1109/access.2020.2986813 fatcat:ikcz6pvxfbhl5c2ohevtyx2o3i

A Data Stream Clustering Algorithm Based Extension of Grid and Density

Yang Yongbin, Ding Mingyong
2013 Research Journal of Applied Sciences Engineering and Technology  
window mechanism and suggest improvements based on the mesh density of the data stream real-time clustering algorithm framework and the various parts of concrete realization of the algorithm.  ...  This study focuses on the summary data structure design and optimize the method of calculation of the mesh density and how to effectively deal with the problem of boundary points, combined with the sliding  ...  P1 and grid cluster P2 cluster connectivity and high; the contrary, the cluster center for the P1, P3 on the cluster and the cluster center between clusters connected by the grid B medium density grid  ... 
doi:10.19026/rjaset.6.4067 fatcat:cm6ruykpybh67m36g6lyaomzpe

Big Data Platform Access Control Rule Generation Method Based on Data Mining

Chun MENG, Zi-xiao KONG, Long YOU, Zuo LUO
2019 DEStech Transactions on Computer Science and Engineering  
The experiment results verify the effectiveness and practicability of the method which can provide accurate access control for the big data platform.  ...  By selecting appropriate data preprocessing, clustering analysis, association analysis algorithms and improving them, we dig out the normal access behavior rules of users from the user logs and attributes  ...  In the correlation analysis stage, the legal user set is obtained from the high-density cluster obtained by cluster analysis.  ... 
doi:10.12783/dtcse/icaic2019/29412 fatcat:g2hzzu5fqzbffd2iy3q3omlety

The Research of Data Mining in Traffic Flow Data

Xu Luhang
2015 International Journal of Database Theory and Application  
Firstly, the data necessary preprocessing, making the data in the form of data mining algorithms can be used directly, followed by K-means algorithm for clustering processing toll stations , and clustering  ...  Intelligent transport, traffic flow data analysis is very important, then how quickly and intelligently analyze traffic data flow has been a problem.  ...  and high-dimensional data has become more mainstream, these large amounts of data and its high-dimensional data analysis features make traditional means powerless.  ... 
doi:10.14257/ijdta.2015.8.4.03 fatcat:qh3aq7xzdvh6rmqsld6mikdgfu

Three-Dimensional Neurophenotyping of Adult Zebrafish Behavior

Jonathan Cachat, Adam Stewart, Eli Utterback, Peter Hart, Siddharth Gaikwad, Keith Wong, Evan Kyzar, Nadine Wu, Allan V. Kalueff, Alessandro Bartolomucci
2011 PLoS ONE  
mapped individual endpoints to 3D reconstructions, and performed cluster analysis to reconfirm behavioral correlates of high-and low-anxiety states.  ...  The application of 3D swim path reconstructions consolidates behavioral data (while increasing data density) and provides a novel way to examine and represent zebrafish behavior.  ...  For example, movement pattern analysis was successfully applied to medaka fish to create accurate predictive models of fish movement based on high-density trajectory data sets [79, 83, 85] .  ... 
doi:10.1371/journal.pone.0017597 pmid:21408171 pmcid:PMC3049776 fatcat:c7luketbanglxhspbgh6hwxkvy

Time Series Forecasting Model Based on SVM with Error Correction of Selected Parameter

2016 Revista Técnica de la Facultad de Ingeniería Universidad del Zulia  
Then with space density clustering method reduces the influence of the noise data.  ...  First, the historical data is for standardizing treatment to remove the smaller noise of signal within the prescribed scope.  ...  Some data of high dimension and including noise points and the boundary point, the clustering effect becomes more obvious.  ... 
doi:10.21311/001.39.5.35 fatcat:uewvknhlvzekjcgdrcg3ktp3km

The Improved DBSCAN Algorithm Study on Maize Purity Identification [chapter]

Pan Wang, Shuangxi Liu, Mingming Liu, Qinxiang Wang, Jinxing Wang, Chunqing Zhang
2012 IFIP Advances in Information and Communication Technology  
The color features parameters of the RGB, HIS and Lab color models of maize crown core area were extracted, while H, S and B as to be the effective characteristic vector after data analysis.  ...  The abnormal points of different density characteristic vector points were separated by FFT. Then clustering results were combined after local density cluster by DBSCAN.  ...  The authors would like to thank Project supported by the Shandong Province Innovation Fund for Post-doctoral (200903031).  ... 
doi:10.1007/978-3-642-27278-3_67 fatcat:jymkwnkoijgitosdb5sndxnhpy

Automated CFD Analysis of Two-Dimensional High-Lift Flows

Anutosh Moitra
2002 Journal of Aircraft  
Introduction CAPABILITY for routine and accurate analysis of aircraft high-lift systems performance is an indispensable requirement for designing aircraft for optimal performance in landing and takeoff  ...  A full three-dimensional analysis capability will be ultimately re- quired for comprehensive analysis of these flow features for air- craft in high-lift configurations.  ... 
doi:10.2514/2.2979 fatcat:educqf45t5ajvp72ockjta36yy

Extracting Operating Modes from Building Electrical Load Data

Stephen Frank, Luigi Gentile Polese, Emily Rader, Michael Sheppy, Jeff Smith
2011 2011 IEEE Green Technologies Conference (IEEE-Green)  
Using clustering to extract operating modes from electrical load data can provide valuable insights into device behavior and identify opportunities for energy savings.  ...  We present a fast and effective heuristic clustering method to identify and extract operating modes in electrical load data.  ...  Lobato of NREL for his assistance with data collection throughout the project. The authors also thank Dr. L.  ... 
doi:10.1109/green.2011.5754872 fatcat:zggwc7jkujgmditqrzonazlfw4
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