Abstract
Text data are copiously found in many domains, such as the Web, social networks, newswire services, and libraries. With the increasing ease in archival of human speech and expression, the volume of text data will only increase over time. This trend is reinforced by the increasing digitization of libraries and the ubiquity of the Web and social networks.
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Notes
- 1.
As discussed in Chap. 6, standard agglomerative algorithms require more than quadratic time, though some simpler variants of single-linkage clustering [469] can be implemented in approximately quadratic time.
- 2.
While the document-term matrix is square in this specific toy example, this might not be the case in general because the corpus size \(n\), and the lexicon size \(d\) are generally different.
- 3.
The original work [271] uses an asymmetric generative process, which is equivalent to the (simpler) symmetric generative process discussed here.
- 4.
The presented factorization for PLSA is approximately optimal, but not exactly optimal.
- 5.
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© 2015 Springer International Publishing Switzerland
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Aggarwal, C. (2015). Mining Text Data. In: Data Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-14142-8_13
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DOI: https://doi.org/10.1007/978-3-319-14142-8_13
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