We investigate randomised algorithms for subset matching with spatial point sets---given two sets of d-dimensional points: a data set T consisting of n points and a pattern P consisting of m points, find the largest match for a subset of the pattern in the data set. This problem is known to be 3-SUM hard and so unlikely to be solvable exactly in subquadratic time. We present an efficient bit-parallel O(nm) time algorithm and an O(n log(m)) time solution based on correlation calculations using fast Fourier transforms. Both methods are shown experimentally to give answers within a few percent of the exact solution and provide a considerable practical speedup over existing deterministic algorithms.
|Translated title of the contribution||Fast Approximate Point Set Matching for Information Retrieval|
|Title of host publication|| SOFSEM 2007: Theory and Practice of Computer Science|
|Subtitle of host publication||33rd Conference on Current Trends in Theory and Practice of Computer Science, Harrachov, Czech Republic, January 20-26, 2007. Proceedings|
|Publisher||Springer Berlin Heidelberg|
|Number of pages||12|
|Publication status||Published - 2007|
|Name||Lecture Notes in Computer Science|