@inproceedings{park01,
abstract = {Most search engines return a lot of unwanted
information. A more thorough filtering process can
be performed on this information to sort out the
relevant documents. A new method called Frequency
Domain Scoring (FDS), which is based on the Fourier
Transform is proposed. FDS performs the filtering
by examining the locality of the keywords throughout
the documents. This is examined and compared to the
well known techniques Latent Semantic Indexing (LSI)
and Cosine measure. We found that FDS obtains
better results of how relevant the document is to
the query. The other two methods (cosine measure,
LSI) do not perform as well mainly because they need
a wider variety of documents to determine the
topic.},
author = {Laurence A.~F.~Park and Marimuthu Palaniswami and Ramamohanarao Kotagiri},
booktitle = {Principles of Data Mining and Knowledge Discovery},
date-modified = {2007-10-19 12:19:33 +1000},
doi = {10.1007/3-540-44794-6},
editor = {Luc de Raedt and Arno Siebes},
keywords = {information retrieval; laurence},
month = {September},
number = {2168},
pages = {362-373},
publisher = {Springer-Verlag},
series = {Lecture Notes in Artificial Intelligence},
title = {Internet Document Filtering using {F}ourier Domain Scoring},
url = {http://www.cs.mu.oz.au/~lapark/PKDD01_Park.pdf},
year = {2001},
bdsk-url-1 = {http://www.cs.mu.oz.au/~lapark/PKDD01_Park.pdf},
bdsk-url-2 = {http://dx.doi.org/10.1007/3-540-44794-6}
}
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