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TI-DBSCAN: Clustering with DBSCAN by Means of the Triangle Inequality
2010Grouping data into meaningful clusters is an important data mining task. DBSCAN is recognized as a high quality density-based algorithm for clustering data. It enables both the determination of clusters of any shape and the identification of noise in data.
Marzena Kryszkiewicz, Piotr Lasek
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AA-DBSCAN: an approximate adaptive DBSCAN for finding clusters with varying densities
The Journal of Supercomputing, 2018Clustering is a typical data mining technique that partitions a dataset into multiple subsets of similar objects according to similarity metrics. In particular, density-based algorithms can find clusters of different shapes and sizes while remaining robust to noise objects.
Jeong-Hun Kim +3 more
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Hashing-Based Approximate DBSCAN
2016Analyzing massive amounts of data and extracting value from it has become key across different disciplines. As the amounts of data grow rapidly, however, current approaches for data analysis struggle. This is particularly true for clustering algorithms where distance calculations between pairs of points dominate overall time.
Tianrun Li, Thomas Heinis, Wayne Luk
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DBSCAN-GM: An improved clustering method based on Gaussian Means and DBSCAN techniques
2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES), 2012Clustering is one of the most useful methods of intelligent engineering domain, in which a set of similar objects are categorized into clusters. Almost all of the well-known clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, the majority is not robust enough
Abir Smiti, Zied Elouedi
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การเปรียบเทียบวิธีการระบุค่าพารามิเตอร์ในวิธี DBSCAN
งานวิจัยนี้มีวัตถุประสงค์เพื่อเปรียบเทียบประสิทธิภาพของวิธีการระบุค่าพารามิเตอร์ Eps ในเทคนิคการจัดกลุ่มแบบ DBSCAN ซึ่งวิธีที่ใช้ในการเปรียบเทียบมี 11 วิธี ได้แก่ วิธีของ Daszykowski วิธี mean วิธี median วิธี P75 วิธี P95 วิธีของ Xia วิธีที่ประยุกต์จากวิธีของ Xia (Xia mean Xia median Xia P75 และ Xia P95) และวิธีของ Karami โดยพิจารณาประส ...openaire +1 more source
2006
Grouping data into meaningful clusters belongs to important tasks in the area of artificial intelligence and data mining. DBSCAN is recognized as a high quality scalable algorithm for clustering data. It enables determination of clusters of any shape and identification of noise data.
Marzena Kryszkiewicz, Łukasz Skonieczny
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Grouping data into meaningful clusters belongs to important tasks in the area of artificial intelligence and data mining. DBSCAN is recognized as a high quality scalable algorithm for clustering data. It enables determination of clusters of any shape and identification of noise data.
Marzena Kryszkiewicz, Łukasz Skonieczny
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Gb-Dbscan: A Fast Granular-Ball Based Dbscan Clustering Algorithm
2023DongDong Cheng +4 more
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DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark
International Journal of High Performance Computing and Networking, 2019Guangsheng Chen +2 more
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Improved fast DBSCAN algorithm
Journal of Computer Applications, 2009Gui-zhi WANG, Guang-liang WANG
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$$\lambda $$-DBSCAN: Augmenting DBSCAN with Prior Knowledge
Joel Dierkes +2 moreopenaire +1 more source