| 摘要 |
A partition-based high dimensional similarity join method allowing similarity to be efficiently measured by beforehand dynamically selecting space partitioning dimensions and the number of the partitioning dimensions using a dimension selection algorithm. A method of efficiently performing similarity join for high dimensional data during a relatively short period of time without requiring massive storage space. The method includes according to the present invention comprises the steps of partitioning a high dimensional data space and performing joins between predetermined data sets. Dimensions for use in partitioning the high dimensional data space and the number of partitioning dimensions are determined in advance before the space partitioning, and the joins are performed only when respective cells of the data sets are overlapping with each other or are neighboring each other.
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