Abstract
Distance and similarity measures are essential to solve many pattern recognition problems such as classification, information retrieval and clustering, where the use of a specific distance could led to a better performance than others. A weighted cosine distance is proposed considering a variation in the weights of exclusive attributes of the input vectors. An agglomerative hierarchical clustering of documents was used for the comparison between the traditional cosine similarity and the one proposed in this paper. This modified measure has outcome in an improvement in the formation of clusters.
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Acknowledgments
This work was partially supported by the Mexican Government (CONACYT projects 240844, SNI, COFAA-IPN, SIP-IPN 20181849, 20171813, BEIFI 20181315).
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Martín-del-Campo-Rodríguez, C., Sidorov, G., Batyrshin, I. (2018). Enhancement of Performance of Document Clustering in the Authorship Identification Problem with a Weighted Cosine Similarity. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Computational Intelligence. MICAI 2018. Lecture Notes in Computer Science(), vol 11289. Springer, Cham. https://doi.org/10.1007/978-3-030-04497-8_4
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