| 摘要 |
A method for hierarchical clustering comprises multi-level partitioning of data points into non-overlapping LDFs and formation of sets of such LDFs, wherein original data points may reside in different LDFs. The sets of LDFs are then hierarchically processed into sets of super-LDFs, which are further processed up the hierarchy. In some embodiments, the further processing ends with the construction of a single super-LDF at the root of the hierarchy. The partitioning of the data points and the hierarchical processing of the sets of LDFs and super-LDFs uses respective local affinity matrices.
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