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Using Association Rules and Logical Learning for Clustering

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dc.contributor.advisor Song, Fei
dc.contributor.advisor Swayne, David
dc.contributor.author Liu, Jia
dc.date.accessioned 2014-09-03T19:42:54Z
dc.date.available 2014-09-03T19:42:54Z
dc.date.copyright 2014-08
dc.date.created 2014-08-21
dc.date.issued 2014-09-03
dc.identifier.uri http://hdl.handle.net/10214/8402
dc.description.abstract This thesis tests whether the apriori algorithm, together with Current Best Hypothesis logical learning, can cluster data such as environmental data. The procedure is tested on a large, sparse dataset describing the ecological impact of agriculture on macroinvertebrate populations in the theTaiZi River Basin of the People’s Republic of China. We compare the association rules / CBH results with two other established algorithms. We demonstrate that our results are a considerable improvement over the other methods tested. We confirm the hypothesis that a threshold of roughly 21% or more of land area devoted to agriculture in this watershed is the “tipping point” to surface water degradation. So-called stochastic dominance is used to further understand the utility of agricultural land use and mitigation procedures such as riparian barriers to intercept and treat runoff. The new algorithm has been successful, but more study of the various apriori parameters is necessary. en_US
dc.language.iso en en_US
dc.subject Data mining en_US
dc.subject Cluster analysis en_US
dc.subject Apriori en_US
dc.subject Current Best Hypothesis en_US
dc.subject Graham Scan en_US
dc.subject PC-ord en_US
dc.subject K-means en_US
dc.title Using Association Rules and Logical Learning for Clustering en_US
dc.type Thesis en_US
dc.degree.programme Computer Science en_US
dc.degree.name Master of Science en_US
dc.degree.department School of Computer Science en_US
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