Subspace similarity search: efficient k-NN queries in arbitrary subspaces
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Scalable density-based subspace clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
Progressive processing of subspace dominating queries
The VLDB Journal — The International Journal on Very Large Data Bases
A survey on unsupervised outlier detection in high-dimensional numerical data
Statistical Analysis and Data Mining
Probabilistic top-k dominating queries in uncertain databases
Information Sciences: an International Journal
| Hi-index | 0.00 |
In this paper, we introduce the partial vector approximation file, an extension of the well known vector approximation file that is constructed to efficiently answer partial similarity queries in any possible subspace which is not known beforehand. The idea of the partial VA-File is to divide the VA-File into a separate file for each dimension and only load the dimensions that are necessary to answer the query. Thus, the partial VA-File is constructed to improve the query performance for systems that have to cope with a wide variety of previously unknown query subspaces. We propose novel algorithms for partial kNN and å-range queries based on the new partial VA-File. In our experiments, we demonstrate that our proposed partial VA-File with the novel algorithms improves the average query performance in comparison to the original VA-File when answering partial similarity queries.