Abstract
Text clustering has become an important challenge in text mining and machine learning, which partitions a specific documents’ collection into groups according to certain similarity/dissimilarity criterion. With advances in information acquisition technologies, textual data can frequently be represented using different techniques, generating multi-view data. We propose in this chapter an overview of the existing clustering methods with a special emphasis on multi-view text clustering methods. We design a new categorizing model based on the main properties pointed out in the multi-view textual clustering method. To evaluate their performance, we perform extensive experiments on several real-world textual data sets. Based on the experimental results, we provide some insights for researchers who want to decide the best method to use when a task of clustering multi-view textual data is required.
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Fraj, M., HajKacem, M.A.B., Essoussi, N. (2022). An Overview of Multi-View Methods for Text Clustering. In: Alyoubi, B., Ben Ncir, CE., Alharbi, I., Jarboui, A. (eds) Machine Learning and Data Analytics for Solving Business Problems. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-031-18483-3_8
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