Comparative Study of Clustering Algorithms using OverallSimSUX Similarity Function for XML Documents
Fuente:
Redalyc
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Artículo científico |
| Lenguaje: | en |
| Publicado: |
Asociación Española para la Inteligencia Artificial
2015
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1876483664055894016 |
|---|---|
| author | Damny Magdaleno |
| author_facet | Damny Magdaleno |
| contents | Comparative Study of Clustering Algorithms using OverallSimSUX Similarity Function for XML Documents Damny Magdaleno Yadriel Miranda Ivett E. Fuentes María M. García Ingeniería XML Clustering Comparative study A huge amount of information is represented in XML format. S everal tools have been developed to store, and query XML data. It becomes inevitable to develop high performance techniques for efficiently analysing extremel y 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 a nd 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 an d 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 rep laced this algorithm by other with dissimilar characteristics. Therefore to endorse completely the methodology, i n 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 d ifferences respect to the K - Star and G - Star clustering algorithm s , according to conclusions achi eved by Nemenyi's post - hoc test . ; (2) For each analysed algorithm when using the methodology , we obtain better results than when it is not taken into account. 2015 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92538718006 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.55 Vol.18 |
| format | Artículo científico |
| id | redalyc_92538718006 |
| institution | Redalyc |
| language | en |
| publishDate | 2015 |
| publisher | Asociación Española para la Inteligencia Artificial |
| spellingShingle | Comparative Study of Clustering Algorithms using OverallSimSUX Similarity Function for XML Documents Damny Magdaleno Ingeniería XML Clustering Comparative study Comparative Study of Clustering Algorithms using OverallSimSUX Similarity Function for XML Documents Damny Magdaleno Yadriel Miranda Ivett E. Fuentes María M. García Ingeniería XML Clustering Comparative study A huge amount of information is represented in XML format. S everal tools have been developed to store, and query XML data. It becomes inevitable to develop high performance techniques for efficiently analysing extremel y 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 a nd 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 an d 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 rep laced this algorithm by other with dissimilar characteristics. Therefore to endorse completely the methodology, i n 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 d ifferences respect to the K - Star and G - Star clustering algorithm s , according to conclusions achi eved by Nemenyi's post - hoc test . ; (2) For each analysed algorithm when using the methodology , we obtain better results than when it is not taken into account. 2015 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92538718006 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.55 Vol.18 |
| title | Comparative Study of Clustering Algorithms using OverallSimSUX Similarity Function for XML Documents |
| topic | Ingeniería XML Clustering Comparative study |
| url | https://www.redalyc.org/articulo.oa?id=92538718006 |