Open AccessProceedings Article
A density-based algorithm for discovering clusters in large spatial Databases with Noise
Martin Ester,Hans-Peter Kriegel,Jörg Sander,Xiaowei Xu +3 more
- pp 226-231
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TLDR
DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.Abstract:
Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLARANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.read more
Citations
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Journal ArticleDOI
Robustness of density-based clustering methods with various neighborhood relations
Efendi Nasibov,Gözde Ulutagay +1 more
TL;DR: The main characteristic of the FN-DBSCAN algorithm is that it combines the speed of the DBSCAN and robustness of the NRFJP (noise robust fuzzy joint points) algorithms.
Proceedings ArticleDOI
Automatic Detection of Phishing Target from Phishing Webpage
Gang Liu,Bite Qiu,Liu Wenyin +2 more
TL;DR: An approach to identification of the phishing target of a given (suspicious) webpage is proposed by clustering the webpage set consisting of its all associated webpages and the given webpage itself by employing a DBSCAN clustering method.
Journal ArticleDOI
Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data.
Máté E. Maros,David Capper,David Capper,David T.W. Jones,Volker Hovestadt,Volker Hovestadt,Volker Hovestadt,Andreas von Deimling,Andreas von Deimling,Stefan M. Pfister,Stefan M. Pfister,Axel Benner,Manuela Zucknick,Martin Sill +13 more
TL;DR: This work compares several ML and calibration algorithms for classifying tumor DNA methylation profiles and provides workflows for selecting, training and calibrating ML algorithms to generate well-calibrated multiclass probability estimates.
Journal ArticleDOI
Simulation of DNA damage clustering after proton irradiation using an adapted DBSCAN algorithm
TL;DR: The algorithm was used to determine the damage concentration clusters and thus to deduce the DSB/SSB ratios created by protons between 500keV and 50MeV, and compared to other calculations and to available experimental data of fibroblast and plasmid cells irradiations.
Proceedings ArticleDOI
gSkeletonClu: Density-Based Network Clustering via Structure-Connected Tree Division or Agglomeration
TL;DR: This paper proposes a novel density-based network clustering algorithm, called gSkeletonClu (graph-skeleton based clustering), which can find the optimal parameter $\varepsilon$ and detect communities, hubs and outliers in large-scale undirected networks automatically without any user interaction.
References
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Finding Groups in Data: An Introduction to Cluster Analysis
TL;DR: An electrical signal transmission system, applicable to the transmission of signals from trackside hot box detector equipment for railroad locomotives and rolling stock, wherein a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count.
Proceedings ArticleDOI
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Proceedings Article
Efficient and Effective Clustering Methods for Spatial Data Mining
Raymond T. Ng,Jiawei Han +1 more
TL;DR: The analysis and experiments show that with the assistance of CLAHANS, these two algorithms are very effective and can lead to discoveries that are difficult to find with current spatial data mining algorithms.
Journal ArticleDOI
An introduction to spatial database systems
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