Abstract
This paper evaluates through an empirical study eight different distance measures used on the LDA + K-means model. We performed our analysis on two miscellaneous datasets that are commonly used. Our experimental results indicate that the probabilistic-based distance measures are better than the vector based distance measures including Euclidean when it comes to cluster a set of documents in the topic space. Moreover, we investigate the implication of the number of topics and show that K-means combined to the results of the Latent Dirichlet Allocation model allows us to have better results than the LDA + Naive and Vector Space Model.
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Bui, Q.V., Sayadi, K., Amor, S.B., Bui, M. (2017). Combining Latent Dirichlet Allocation and K-Means for Documents Clustering: Effect of Probabilistic Based Distance Measures. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_24
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