A master thesis from Aalborg University
'Practical Data Mining on a Swiss Flora Database and Geographical Clustering Software Implementation'
Author(s)
Term
4. term
Education
Publication year
2006
Submitted on
2012-02-14
Abstract
'This thesis document deals with the context of data mining. We explain various clustering techniques such as partition-based techniques and probabilistic approaches. We have implemented the k-means clustering algorithm, the trimmed k-means variation of it and the Naïve Bayes with EM learning for clustering. We have compared the results of these algorithms applied to the given database. Furthermore, we have implemented a tool where these clustering techniques can be applied, and the results of those techniques can be shown on a map of the geographical area where the data come from. '
Documents
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