7 results for “DBSCAN”

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Research NotesOpen accessIn the keywordsReal Time Density-Based Clustering (RTDBC) Algorithm for Big DataDr. B. Ravi PrasadDensity Based Spatial Clustering of Applications with Noise (DBSCAN), a well-known Density-Based Clustering Algorithm is a advanced data clustering method with various applications in numerous fields like Satellites images, X-ray crystallography, Anomaly Detection in Temperature Data. But its run time R(n2) complexity draws a major challenge. In this research paper, we propose a new unique algorithm called Real Time Density … …Journal of Computer Science, Vol. 13, Issue 10 · 15 Aug 2017 · 7,298 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsStar Catalog Generation for Satellite Attitude Navigation Using Density Based ClusteringMuhammad Arif Saifudin, Bib Paruhum Silalahi, Imas Sukaesih SitanggangA new method to generate star catalog using density-based clustering is proposed. It identifies regions of a high star density by using Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm. Reducing the number stars performed by storing the brightest star in each cluster. The brightest star and all non-clustered members are then stored as a navigation star candidate. Monte … …Journal of Computer Science, Vol. 11, Issue 12 · 1 Feb 2016 · 6,970 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsIdentification of Fraud in Accidental and Healthcare Insurance Using Local Outlier Factor: A Machine Learning ApproachJyoti Lele, Vaidehi Deshmukh, Abhinav Chandra, Radhika DesaiAn unsupervised machine learning model that uses the mechanism of the Local Outlier Factor to flag and detect ambiguous as well as potentially fraudulent claims in Accidental and Healthcare insurance is proposed in this paper. It entirely automates the manual investigation of claims by claim appraisers in any organization. The ethos of this model is to comprehensively automate and expedite … …Journal of Computer Science, Vol. 21, Issue 12 · 22 Jan 2026 · 1,575 accessesView PDF Cite

Research ArticleOpen accessIn the abstractMANAT: A Filtering-Based Method for Denoising Nonuniform Photogrammetric Point CloudsYun Sin Chong, Hui Hui Wang, Yin Chai WangThree-dimensional point clouds reconstructed from photogrammetry often exhibit noise and non-uniform sampling density, which challenges existing denoising methods that rely on precise normal estimation or extensive parameter tuning. This study presents the Multi Attribute Neighbour Attraction Technique (MANAT), a novel single-stage, density-adaptive filtering method that jointly leverages spatial position, surface normals, and color as inherent photogrammetric attributes for unified noise … …Journal of Computer Science, Vol. 22, Issue 4 · 28 Apr 2026 · 1,524 accessesView PDF Cite

Research ArticleOpen accessIn the abstractA Fuzzy Clustering Based Method for the Spatio-Temporal Data AnalysisAnahita Zolghadr, Afsaneh JalalianSpatiotemporal data is a type of data that is collected by the sensors. This type of data has two spatial and temporal dimensions. There are many challenges in analyzing spatiotemporal big data. Common evaluation metrics of clustering methods are not appropriate for spatiotemporal data. Previous clustering methods and the conventional evaluation metrics are efficient for data like time series with … …Journal of Computer Science, Vol. 18, Issue 6 · 13 Jun 2022 · 5,638 accessesView PDF Cite

Research ArticleOpen accessIn the abstractA Density Based Dynamic Data Clustering Algorithm based on Incremental DatasetS. Angel Latha Mary, K.R. Shankar KumarProblem statement: Clustering and visualizing high-dimensional dynamic data is a challenging problem. Most of the existing clustering algorithms are based on the static statistical relationship among data. Dynamic clustering is a mechanism to adopt and discover clusters in real time environments. There are many applications such as incremental data mining in data warehousing applications, sensor network, which relies on dynamic … …Journal of Computer Science, Vol. 8, Issue 5 · 20 Feb 2012 · 7,465 accessesView PDF Cite

Research ArticleOpen accessIn the abstractEnhanced Clustering Techniques for Hyper Network Planning using Minimum Spanning Trees and Ant-Colony AlgorithmLamiaa Fattouh IbrahimProblem statement: The process of network planning is divided into two sub steps. The first step is determining the location of the Multi Service Access Node (MSAN). The second step is the construction of subscriber network lines from MSAN to subscribers to satisfy optimization criteria and design constraints. Due to the complexity of this process artificial intelligence and clustering techniques … …Journal of Computer Science, Vol. 7, Issue 3 · 9 Mar 2011 · 6,813 accessesView PDF Cite