Vector Approximation based Indexing for Non-uniform High Dimensional Data Sets

With the proliferation of multimedia data, there is increasing need to support the indexing and searching of high dimensional data. Recently, a vector approximation based technique called VA- le has been proposed for indexing high dimensional data. Ithas been shown that the VA- le is an e ective technique compared to the current approaches based on space and data partitioning. The VA- le gives good performance especially when the data set is uniformly distributed. Real data sets are not uniformly distributed, are often clustered, and the dimensions of the feature vectors in real data sets are usually correlated. More careful analysis for nonuniform or correlated data is needed for e ectively indexing high dimensional data. We propose a solution to these problems and propose the VA+- le, a new technique for indexing high dimensional data sets based on vector approximations. We conclude with an evaluation of nearest neighbor queries and show that the VA+- le technique results in signi c...

• Type: Conference
• Year: 2000
• Where: CIKM
• Authors: Hakan Ferhatosmanoglu, Ertem Tuncel, Divyakant Agrawal, Amr El Abbadi
• Keywords: CIKM 2000, Data Sets, Information Management, Real Data Sets, Vector Approximations