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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
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The R*-tree: an efficient and robust access method for points and rectangles
TLDR
The R-tree, one of the most popular access methods for rectangles, is based on the heuristic optimization of the area of the enclosing rectangle in each inner node. Expand
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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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A Three-Way Model for Collective Learning on Multi-Relational Data
TLDR
We present a novel approach to relational learning based on the factorization of a three-way tensor. Expand
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Integrating structured biological data by Kernel Maximum Mean Discrepancy
MOTIVATION Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-basedExpand
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The X-tree : An Index Structure for High-Dimensional Data
TLDR
In this paper, we propose a new method for indexing large amounts of point and spatial data in high-dimensional space. Expand
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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
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Shortest-path kernels on graphs
  • K. Borgwardt, H. Kriegel
  • Mathematics, Computer Science
  • Fifth IEEE International Conference on Data…
  • 27 November 2005
TLDR
We propose graph kernels based on shortest paths, which are polynomial to compute, positive definite and retain expressivity while avoiding the phenomenon of ”tottering”. 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
  • 1,245
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