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- W4300967890 abstract "The main characteristic of the density-based spatial clustering application with noise (DBSCAN) algorithm is used to detect the points which lie outside the dense regions are considered as outliers or noisy points. It perfectly fits to outlier detection and from the cluster with different shape and size. This chapter explores the major steps that are followed during the DBSCAN algorithm. The two-user defined input parameters considered for DBSCAN algorithm for clustering: epsilon and minimum points. A cluster is considered when minimum number of points equals the epsilon distance of core point. The three types of points upon applying a DBSCAN algorithm to a particular dataset – core point, border point, and noise point. The different complexities of the algorithm are best case, worst case and average case. The chapter also explores the advantages and disadvantages of the DBSCAN algorithm." @default.
- W4300967890 created "2022-10-04" @default.
- W4300967890 date "2022-08-26" @default.
- W4300967890 modified "2023-09-28" @default.
- W4300967890 title "Outlier Analysis" @default.
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- W4300967890 doi "https://doi.org/10.1002/9781119858010.ch5" @default.
- W4300967890 hasPublicationYear "2022" @default.
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