J. Sander

Publications111
h-index43
Citations29,913
Highly Influential Citations3,878
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A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
TLDR
We present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. Expand
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LOF: identifying density-based local outliers
TLDR
We show that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier. Expand
  • 3,193
  • 612
  • PDF
OPTICS: ordering points to identify the clustering structure
TLDR
We introduce a new algorithm 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. Expand
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  • 349
  • PDF
Density-Based Clustering Based on Hierarchical Density Estimates
TLDR
We propose 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. Expand
  • 492
  • 93
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
TLDR
The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. Expand
  • 1,101
  • 86
  • PDF
LOF: identifying density-based local outliers
TLDR
We show that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier. Expand
  • 1,245
  • 64
Incremental Clustering for Mining in a Data Warehousing Environment
TLDR
We present the first incremental clustering algorithm which is applicable to any database containing data from a metric space, e.g., to a spatial database or a WWW-log database. Expand
  • 526
  • 45
  • PDF
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 in this article. Expand
  • 229
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DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN
TLDR
We show that the original DBSCAN algorithm with effective indexes and reasonably chosen parameter values performs competitively compared to the method proposed by Gan and Tao. Expand
  • 310
  • 28
  • PDF
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
TLDR
In this paper, we perform an extensive experimental study on the performance of a representative set of standard k nearest neighborhood-based methods for unsupervised outlier detection, across a wide variety of datasets prepared for this purpose. Expand
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