• Publications
  • Influence
ELKI: A Software System for Evaluation of Subspace Clustering Algorithms
TLDR
In order to establish consolidated standards in novel data mining areas, newly proposed algorithms need to be evaluated thoroughly. Expand
  • 96
  • 12
  • PDF
Robust, Complete, and Efficient Correlation Clustering
TLDR
We propose the novel correlation clustering algorithm COPAC (COrrelation PArtition Clustering) that aims at improved robustness, completeness, usability, and efficiency. Expand
  • 65
  • 6
  • PDF
Efficient reverse k-nearest neighbor search in arbitrary metric spaces
TLDR
We propose the first approach for efficient RkNN search in arbitrary metric spaces where the value of k is not known in advance and may change from query to query. Expand
  • 134
  • 6
  • PDF
Interactive data mining with 3D-parallel-coordinate-trees
TLDR
Parallel coordinates are an established technique to visualize high-dimensional data, in particular for data mining purposes. Expand
  • 82
  • 6
Finding Hierarchies of Subspace Clusters
TLDR
In this paper, we propose the algorithm HiSC (Hierarchical Subspace Clustering) that can detect hierarchies of nested subspace clusters, i.e. the relationships of lower-dimensional sub space clusters that are embedded within higher-dimensional Subspace clusters. Expand
  • 59
  • 6
  • PDF
Detection and Visualization of Subspace Cluster Hierarchies
TLDR
We propose the algorithm DiSH (Detecting Subspace cluster Hierarchies) that improves in the following points over existing approaches: First, DiSH uncovers complex hierarchies of nested subspace clusters, i.e. clusters in lower-dimensional subspaces that are embedded within higher-dimensional Subspace clusters. Expand
  • 83
  • 5
  • PDF
Global Correlation Clustering Based on the Hough Transform
TLDR
We propose an efficient and effective method for finding arbitrarily oriented subspace clusters of different dimensionality even if they are sparse or are intersected by other clusters within a noisy environment. Expand
  • 49
  • 5
  • PDF
Reverse k-nearest neighbor search in dynamic and general metric databases
TLDR
In this paper, we propose an original solution for the general reverse k-nearest neighbor (RkNN) search problem. Expand
  • 58
  • 4
  • PDF
DeLi-Clu: Boosting Robustness, Completeness, Usability, and Efficiency of Hierarchical Clustering by a Closest Pair Ranking
TLDR
We introduce a novel hierarchical clustering algorithm DeLiClu (Density Linked Clustering) that combines the advantages of OPTICS and Single-Link by fading out their drawbacks. Expand
  • 61
  • 3
  • PDF
On Exploring Complex Relationships of Correlation Clusters
TLDR
In high dimensional data, clusters often only exist in arbitrarily oriented subspaces of the feature space. Expand
  • 56
  • 2
  • PDF