Abstract
This chapter is concerned with the unsupervised learning algorithms which are approaches to text clustering.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Grossberg, S.: Competitive learning: from interactive activation to adaptive resonance. Cogn. Sci. 11, 23–63 (1987)
Jo, T.: Neural based approach to keyword extraction from documents. Lect. Note Comput. Sci. 2667, 456–461 (2003)
Jo, T.: The Implementation of Dynamic Document Organization Using the Integration of Text Clustering and Text Categorization, University of Ottawa (2006)
Kohonen, T.: Correlation matrix memories. IEEE Trans. Comput. 21, 353–359 (1972)
Kohonen, T., Kaski, S., Lagus, K., Salojavi, J., Honkela, J.: Self organization of massive document collection. IEEE Trans. Neural Netw. 11, 574–585 (2000)
Martinetz, T., Schulten, K.: A “neural gas” network learns topologies. In: Artificial Neural Networks, pp. 397–402 (1991)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Jo, T. (2019). Text Clustering: Approaches. In: Text Mining. Studies in Big Data, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-319-91815-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-91815-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91814-3
Online ISBN: 978-3-319-91815-0
eBook Packages: EngineeringEngineering (R0)