Document Clustering using K-Means and K-Medoids
Published: 2013
Author(s) Name: Rakesh Chandra Balabantaray, Chandrali Sarma, Monica Jha |
Author(s) Affiliation: 1st Author belongs to IIIT Bhubaneshwar; 2nd & 3rd Author belongs to Guwahati University, India
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Abstract
With the huge upsurge of information in day-to-day’s life,
it has become difficult to assemble relevant information
in nick of time. But people, always are in dearth of
time, they need everything quick. Hence clustering
was introduced to gather the relevant information in
a cluster. There are several algorithms for clustering
information out of which in this paper, we accomplish
K-means and K-Medoids clustering algorithm and a
comparison is carried out to find which algorithm is best
for clustering. On the best clusters formed, document
summarization is executed based on sentence weight
to focus on key point of the whole document, which
makes it easier for people to ascertain the information
they want and thus read only those documents which
is relevant in their point of view.
Keywords: Clustering, K-Means, K-Medoids, WEKA3.9, Document Summarization
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