Pivot Selection Methods Based on Covariance and Correlation for Metric-space Indexing

摘要

Metric-space indexing is a general method for similarity queries of complex data. The quality of the index tree is a critical factor of the query performance. Bulkloading a metric-space indexing tree can be represented by two recursive steps, pivot selection and data partition, while pivot selection dominants the quality of the index tree. Two heuristics, based on covariance and correlation, for pivot selection are proposed. Empirical results show that their performance is superior or comparable to existing methods.