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ACM Transactions on Database Systems, 2017
At SIGMOD 2015, an article was presented with the title “DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation” that won the conference’s best paper award. In this technical correspondence, we want to point out some inaccuracies in the way DBSCAN was represented, and why the criticism should have been directed at the assumption about the ...
Erich Schubert +4 more
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At SIGMOD 2015, an article was presented with the title “DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation” that won the conference’s best paper award. In this technical correspondence, we want to point out some inaccuracies in the way DBSCAN was represented, and why the criticism should have been directed at the assumption about the ...
Erich Schubert +4 more
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G-DBSCAN: An Improved DBSCAN Clustering Method Based On Grid
Advanced Science and Technology Letters, 2014Clustering is one of the most active research fields in data mining. Clustering in statistics, pattern recognition, image processing, machine learning, biology, marketing and many other fields have a wide range of applications. DBSCAN is a density-based clustering algorithm. this algorithm clusters data of high density.
Li Ma +3 more
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Soft DBSCAN: Improving DBSCAN clustering method using fuzzy set theory
2013 6th International Conference on Human System Interactions (HSI), 2013Clustering is one of the most valuable methods of computational intelligence field, in which sets of related objects are cataloged into clusters. Almost all of the well-known clustering algorithms require input number of clusters which is hard to determine but have a significant influence on the clustering result.
Abir Smiti, Zied Eloudi
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Fuzzy Core DBScan Clustering Algorithm
2014In this work we propose an extension of the DBSCAN algorithm to generate clusters with fuzzy density characteristics. The original version of DBSCAN requires two parameters (minPts and ?) to determine if a point lies in a dense area or not. Merging different dense areas results into clusters that fit the underlined dataset densities.
Bordogna Gloria, Ienco Dino, Ienco Dino
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Evolutionary clustering with DBSCAN
2013 Ninth International Conference on Natural Computation (ICNC), 2013Clustering algorithms have been used in the field of data mining for such a long time. With the accumulation of the online data sets, studies on cluster evolution were carried out so as to decrease noise and maintain continuity of clustering results. A number of evolutionary clustering algorithms have been proposed, such as the evolutionary K-means and
Yuchao Zhang, Hongfu Liu, Bo Deng
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Using Greedy algorithm: DBSCAN revisited II
Journal of Zhejiang University-SCIENCE A, 2004The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996), and has the following advantages: first, Greedy algorithm substitutes for R(*)-tree (Bechmann et al., 1990) in DBSCAN to index the clustering space so that the clustering time cost ...
Shi-hong, Yue +3 more
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Scalable fuzzy neighborhood DBSCAN
International Conference on Fuzzy Systems, 2010The majority of data available in most disciplines is unlabeled and unclassified. The amount of data is often massive, hence scalable processing methods are required. One method of providing structure to unlabeled data is to group it by clustering. Density based methods discover the number of clusters.
Jonathon K. Parker +2 more
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Annals of the American Association of Geographers, 2018
Epidemic diffusion is a space–time process, and showing time-series disease maps is a common way to demonstrate an epidemic progression in time and space.
Kuo, Fei-Ying +2 more
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Epidemic diffusion is a space–time process, and showing time-series disease maps is a common way to demonstrate an epidemic progression in time and space.
Kuo, Fei-Ying +2 more
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AF-DBSCAN: An unsupervised Automatic Fuzzy Clustering method based on DBSCAN approach
2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), 2019Automatic clustering problems play an important role to ameliorate the goodness of the data set's partitioning. Actually, the requirement to detect the suitable clustering solution without need for user-given parameters still remain challenging in unsupervised learning.
Sihem Jebari, Abir Smiti, Aymen Louati
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MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data
Frontiers of Computer Science, 2013DBSCAN (density-based spatial clustering of applications with noise) is an important spatial clustering technique that is widely adopted in numerous applications. As the size of datasets is extremely large nowadays, parallel processing of complex data analysis such as DBSCAN becomes indispensable.
He, Yaobin +4 more
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