Clustering the Web: Comparing Clustering Methods in Swedish
Independent thesis Basic level (degree of Bachelor), 12 credits / 18 HE creditsStudent thesisAlternative title
Webbklustring : En jämförelse av klustringsmetoder på svenska (Swedish)
Clustering -- automatically sorting -- web search results has been the focus of much attention but is by no means a solved problem, and there is little previous work in Swedish. This thesis studies the performance of three clustering algorithms -- k-means, agglomerative hierarchical clustering, and bisecting k-means -- on a total of 32 corpora, as well as whether clustering web search previews, called snippets, instead of full texts can achieve reasonably decent results. Four internal evaluation metrics are used to assess the data. Results indicate that k-means performs worse than the other two algorithms, and that snippets may be good enough to use in an actual product, although there is ample opportunity for further research on both issues; however, results are inconclusive regarding bisecting k-means vis-à-vis agglomerative hierarchical clustering. Stop word and stemmer usage results are not significant, and appear to not affect the clustering by any considerable magnitude.
Place, publisher, year, edition, pages
2013. , 37 p.
clustering, web, search results, snippets, k-means, agglomerative hierarchical clustering, bisecting k-means, swedish
Language Technology (Computational Linguistics) Human Computer Interaction
IdentifiersURN: urn:nbn:se:liu:diva-95228ISRN: LIU-IDA/KOGVET-G--13/025--SEOAI: oai:DiVA.org:liu-95228DiVA: diva2:635095
Subject / course
Cognitive science programme
Jönsson, Arne, Professor
Signoret, Carine, Post-Doc