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• W3139958101 abstract "The Multinomial distribution has been widely used to model count data. However, its Naive Bayes assumption usually degrades clustering performance especially when correlation between features is imminent, i.e., text documents. In this paper, we use the Negative Multinomial distribution to perform clustering based on finite mixture models, where the mixture parameters are to be estimated using a novel minorization-maximization algorithm, thriving in high-dimensionality optimization settings. Furthermore, we integrate a model-based feature selection approach to determine the optimal number of components in the mixture. To evaluate the clustering performance of the proposed model, three real-world applications are considered, namely, COVID-19 analysis, Web page clustering and facial expression recognition." @default.
• W3139958101 created "2021-04-13" @default.
• W3139958101 creator A5021806274 @default.
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• W3139958101 date "2021-01-01" @default.
• W3139958101 modified "2023-09-23" @default.
• W3139958101 title "Mixture-Based Unsupervised Learning for Positively Correlated Count Data" @default.
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• W3139958101 doi "https://doi.org/10.1007/978-3-030-73280-6_12" @default.
• W3139958101 hasPublicationYear "2021" @default.
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