Comparative Study of Clustering Algorithms using OverallSimSUX Similarity Function for XML Documents

  • Damny Magdaleno Guevara
  • Yadriel Miranda
  • Ivett Fuentes
  • María Garc ía


A huge amount of information is represented in XML format. Several tools have been developed to store,
and query XML data. It becomes inevitable to develop high performance techniques for efficiently analysing
extremely large collections of XML data. One of the methods that many researchers have focused on is clustering,
which groups similar XML data, according to their content and structures. In previous work, there has been proposed
the similarity function OverallSimSUX, that facilitates to capture the degree of similitude among the documents
with a novel methodology for clustering XML documents using both structural and content features. Although this
methodology shows good performance, endorsed by experiments with several corpus and statistical tests, on having
had impliedly only one clustering algorithm, K-Star, we do not know the effect that it would suffer if we replaced
this algorithm by other with dissimilar characteristics. Therefore to endorse completely the methodology, in this
work we make a comparative study of the effects of applying the methodology for the OverallSimSUX similarity
function calculation, using clustering algorithms of different classifications . Based on our analysis, we arrived to
two important results: (1) The Fuzzy-SKWIC clustering algorithm works best both with methodology and without
methodology, although there are not present significant differences respect to the K-Star clustering algorithm; (2)
For each analysed algorithm when using the methodology, we obtain better results than when it is not taken into


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How to Cite
Magdaleno Guevara, D., Miranda, Y., Fuentes, I., & Garc ía, M. (2015). Comparative Study of Clustering Algorithms using OverallSimSUX Similarity Function for XML Documents. Inteligencia Artificial, 19(57), 69-80. Retrieved from