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A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
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.
LOF: identifying density-based local outliers
TLDR
This paper contends that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier, called the local outlier factor (LOF), and gives a detailed formal analysis showing that LOF enjoys many desirable properties.
OPTICS: ordering points to identify the clustering structure
TLDR
A new algorithm is introduced for the purpose of cluster analysis which does not produce a clustering of a data set explicitly; but instead creates an augmented ordering of the database representing its density-based clustering structure.
Density-Based Clustering Based on Hierarchical Density Estimates
TLDR
This work proposes a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed, and proposes a novel cluster stability measure.
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
TLDR
The generalized algorithm DBSCAN can cluster point objects as well as spatially extended objects according to both, their spatial and their nonspatial attributes, and four applications using 2D points (astronomy, 3D points,biology, 5D points and 2D polygons) are presented, demonstrating the applicability of GDBSCAN to real-world problems.
LOF: identifying density-based local outliers
TLDR
This paper contends that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier, called the local outlier factor (LOF), and gives a detailed formal analysis showing that LOF enjoys many desirable properties.
Incremental Clustering for Mining in a Data Warehousing Environment
TLDR
It can be proven that the incremental algorithm yields the same result as DBSCAN, which is applicable to any database containing data from a metric space, e.g., to a spatial database or to a WWW-log database.
DBSCAN Revisited, Revisited
TLDR
In new experiments, it is shown that the new SIGMOD 2015 methods do not appear to offer practical benefits if the DBSCAN parameters are well chosen and thus they are primarily of theoretical interest.
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection
TLDR
An integrated framework for density-based cluster analysis, outlier detection, and data visualization is introduced, consisting of an algorithm to compute hierarchical estimates of the level sets of a density, following Hartigan’s classic model of density-contour clusters and trees.
A distribution-based clustering algorithm for mining in large spatial databases
TLDR
The new clustering algorithm DBCLASD (Distribution-Based Clustering of LArge Spatial Databases) is introduced to discover clusters of this type and is very attractive when considering its nonparametric nature and its good quality for clusters of arbitrary shape.
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