We show how full-text search based on inverted indices can be accelerated by clustering the documents without losing results (SeCluD -- SEarch with CLUstered Documents). We develop a fast multilevel clustering algorithm that explicitly uses query cost for conjunctive queries as an objective function. Depending on the inputs we get up to four times faster than non-clustered search. The resulting clusters are also useful for data compression and for distributing the work over many machines.
@article{arxiv.1411.1220,
title = {Faster Exact Search using Document Clustering},
author = {Jonathan Dimond and Peter Sanders},
journal= {arXiv preprint arXiv:1411.1220},
year = {2014}
}