A Generic Framework for Efficient Subspace Clustering of High-Dimensional Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Clustering high dimensional data: A graph-based relaxed optimization approach
Information Sciences: an International Journal
Dimensionality reduction for heterogeneous dataset in rushes editing
Pattern Recognition
Heidi matrix: nearest neighbor driven high dimensional data visualization
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
Subspace sums for extracting non-random data from massive noise
Knowledge and Information Systems
Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Mining representative subspace clusters in high-dimensional data
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
A grid-based clustering algorithm for high-dimensional data streams
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Dynamic parallelization of grid–enabled web services
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
A grid-based subspace clustering algorithm for high-dimensional data streams
WISE'06 Proceedings of the 7th international conference on Web Information Systems
Interactive data mining with 3D-parallel-coordinate-trees
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Short communication: Algorithm to determine ε-distance parameter in density based clustering
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
In high-dimensional feature spaces traditional clustering algorithms tend to break down in terms of efficiency and quality. Nevertheless, the data sets often contain clusters which are hidden in various subspaces of the original feature space. In this paper, we present a feature selection technique called SURFING (SUbspaces Relevant For clusterING) that finds all subspaces interesting for clustering and sorts them by relevance. The sorting is based on a quality criterion for the interestingness of a subspace using the k-nearest neighbor distances of the objects. As our method is more or less parameterless, it addresses the unsupervised notion of the data mining task "clustering" in a best possible way. A broad evaluation based on synthetic and real-world data sets demonstrates that SURFING is suitable to find all relevant subspaces in high dimensional, sparse data sets and produces better results than comparative methods.