NIK2009 - Combining Latent Semantic Indexing and Clustering to Retrieve and Cluster Biomedical Infor
|Forfattere||Jon Rune Paulsen, Heri Ramampiaro|
|Publikasjon||Norsk informatikkonferanse (NIK)|
|ISSN/ISSN2||1892-0713 (trykk) / 1892-0721 (online)/|
|Utgiver||Tapir Akademisk Forlag|
|Adresse utgiver||Nardoveien 12 7005 Trondheim|
AbstraktThis paper presents document retrieval approach based on combination of
latent semantic index (LSI) and two different clustering algorithms. The idea
is to first retrieve papers and create initial clusters based on LSI. Then, we
use flat clustering method to further group similar documents in clusters.
The paper also presents a new algorithm for k-means clustering that aims
at dealing with the fact that the standard k-means algorithm is too greedy.
Our experiments show that in many of cases the two-step algorithm performs
better than standard k-means. The main advantage of our method is that it
forces the centroid vector towards the extremities, and consequently gets a
completely different starting point compared to the standard algorithm. This
also makes the algorithm less greedy than the standard one. We believe
our method can be used to retrieve relevant documents from a document
collection. Our experiments have revealed that it performs well in most cases,
but also failing in some cases.
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