Cluster labeling improvement by utilizing data fusion and Wikipedia
| buir.advisor | Can, Fazlı | |
| dc.contributor.author | Ayduğan, Gökçe | |
| dc.date.accessioned | 2017-08-25T11:32:05Z | |
| dc.date.available | 2017-08-25T11:32:05Z | |
| dc.date.copyright | 2017-07 | |
| dc.date.issued | 2017-07 | |
| dc.date.submitted | 2017-08-15 | |
| dc.department | Department of Computer Engineering | en_US |
| dc.description | Cataloged from PDF version of article. | en_US |
| dc.description | Thesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2017. | en_US |
| dc.description | Includes bibliographical references (leaves 53-57). | en_US |
| dc.description.abstract | A cluster is a set of related documents. Cluster labeling is the process of assigning descriptive labels to clusters. This study investigates several cluster labeling approaches and presents novel methods. The rst uses clusters themselves and extracts important terms, which distinguish clusters from each other, with different statistical feature selection methods. Then it applies di erent data fusion methods for combining their outcomes. Our results show that although it provides statistically signi cantly better results for some cases, it is not a stable and reliable labeling method. This can be explained by the fact that a good label may not occur in the cluster at all. The second exploits Wikipedia as an external resource and uses its anchor texts and categories to enrich the label pool. Labeling with Wikipedia anchor text fails because the suggested labels tend to focus on minor topics. Although the minor topics are related to the main topic, they do not exactly describe it. After this observation, we use categories of Wikipedia pages to improve our label pool in two ways. The rst fuses important terms and Wikipedia categories with rank based fusion methods. The second looks relatedness of Wikipedia pages to the clusters and use only categories of related pages. The experimental results show that both methods provide statistically signi - cantly better results than the other cluster labeling approaches that we examine in this study. | en_US |
| dc.description.degree | M.S. | en_US |
| dc.description.statementofresponsibility | by Gökçe Ayduğan. | en_US |
| dc.format.extent | xiii, 69 leaves : charts (some color) ; 29 cm. | en_US |
| dc.identifier.itemid | B156101 | |
| dc.identifier.uri | http://hdl.handle.net/11693/33553 | |
| dc.language.iso | English | en_US |
| dc.publisher | Bilkent University | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Cluster Labeling | en_US |
| dc.subject | Data Fusion | en_US |
| dc.subject | Wikipedia | en_US |
| dc.title | Cluster labeling improvement by utilizing data fusion and Wikipedia | en_US |
| dc.title.alternative | Veri birleştirme ve Wikipedia kullanarak küme etiketlemenin iyileştirilmesi | en_US |
| dc.type | Thesis | en_US |