dc.contributor.advisor | Petersen, Sobah | |
dc.contributor.advisor | Landmark, Andreas | |
dc.contributor.author | Dyvik, Sondre Hoff | |
dc.date.accessioned | 2019-09-11T10:56:15Z | |
dc.date.created | 2017-05-22 | |
dc.date.issued | 2017 | |
dc.identifier | ntnudaim:15965 | |
dc.identifier.uri | http://hdl.handle.net/11250/2615852 | |
dc.description.abstract | When making strategic decisions in a business setting it is advantageous to know
as much about the users of your products as possible. Information about what
interest segments exist can be used for search optimization, product improvement
and custom tailored marketing.
This project belongs to the field of knowledge discovery in databases and concerns
the discovery of user interest clusters in an electronic business reference
system using an implicit voting scheme based on the sytem s web logs. A literature
review is conducted to explore recent efforts in the field, experiments are
conducted to apply the theory from the literature review and a qualitative analysis
is conducted on the results of the experiments.
The main contributions of this thesis are a comparison of Spearman Rank correlation
and Frequency-Weighted Pearson correlation in terms of scalability and
the application of Blondel s algorithm on a previously unexplored data set generated
by users in a professional work setting. The results show that FrequencyWeighted
Pearson correlation is the more scalable alternative, and that clusters
do exist in the data set. Furthermore it is shown that there is seasonal variations
in the data set and the discovered interest groups. | en |
dc.language | eng | |
dc.publisher | NTNU | |
dc.subject | Informatikk, Kunstig intelligens | en |
dc.title | Clustering users in an electronic business reference system | en |
dc.type | Master thesis | en |
dc.source.pagenumber | 132 | |
dc.contributor.department | Norges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi og elektroteknikk,Institutt for datateknologi og informatikk | nb_NO |
dc.date.embargoenddate | 2020-05-22 | |