Finding hierarchies of subspace clusters

Authors:
Elke Achtert;Christian Böhm;Hans-Peter Kriegel;Peer Kröger;Ina Müller-Gorman;Arthur Zimek
Affiliations:
Institute for Informatics, Ludwig-Maximilians-Universität München, Germany;Institute for Informatics, Ludwig-Maximilians-Universität München, Germany;Institute for Informatics, Ludwig-Maximilians-Universität München, Germany;Institute for Informatics, Ludwig-Maximilians-Universität München, Germany;Institute for Informatics, Ludwig-Maximilians-Universität München, Germany;Institute for Informatics, Ludwig-Maximilians-Universität München, Germany
Venue:
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Year:
2006

Citing 7
Cited 7

Automatic subspace clustering of high dimensional data for data mining applications

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Entropy-based subspace clustering for mining numerical data

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A Monte Carlo algorithm for fast projective clustering

Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Density Connected Clustering with Local Subspace Preferences

ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A Generic Framework for Efficient Subspace Clustering of High-Dimensional Data

ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining

ELKI: A Software System for Evaluation of Subspace Clustering Algorithms

SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Detection and visualization of subspace cluster hierarchies

DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Subspace clustering for indexing high dimensional data: a main memory index based on local reductions and individual multi-representations

Proceedings of the 14th International Conference on Extending Database Technology
Advancing data clustering via projective clustering ensembles

Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
A survey on enhanced subspace clustering

Data Mining and Knowledge Discovery
Projective clustering ensembles

Data Mining and Knowledge Discovery
Interactive data mining with 3D-parallel-coordinate-trees

Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many clustering algorithms are not applicable to high-dimensional feature spaces, because the clusters often exist only in specific subspaces of the original feature space. Those clusters are also called subspace clusters. 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 subspace clusters that are embedded within higher-dimensional subspace clusters. Several comparative experiments using synthetic and real data sets show the performance and the effectivity of HiSC.