On principal component analysis, cosine and Euclidean measures in information retrieval
| Autor: | Jorma Laurikkala, Tuomo Korenius, Martti Juhola |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Normalization (statistics)
Information Systems and Management business.industry Pattern recognition Measure (mathematics) Complete linkage Computer Science Applications Theoretical Computer Science Euclidean distance Artificial Intelligence Control and Systems Engineering Principal component analysis Vector space model Artificial intelligence business Cluster analysis Software Ward's method Mathematics |
| Zdroj: | Information Sciences. 177:4893-4905 |
| ISSN: | 0020-0255 |
| DOI: | 10.1016/j.ins.2007.05.027 |
| Popis: | Clustering groups document objects represented as vectors. An extensive vector space may cause obstacles to applying these methods. Therefore, the vector space was reduced with principal component analysis (PCA). The conventional cosine measure is not the only choice with PCA, which involves the mean-correction of data. Since mean-correction changes the location of the origin, the angles between the document vectors also change. To avoid this, we used a connection between the cosine measure and the Euclidean distance in association with PCA, and grounded searching on the latter. We applied the single and complete linkage and Ward clustering to Finnish documents utilizing their relevance assessment as a new feature. After the normalization of the data PCA was run and relevant documents were clustered. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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