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Data Mining pp 429-455 | Cite as

Mining Text Data

  • Charu C. Aggarwal

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.

Keywords

Text Data Cosine Similarity Latent Semantic Analysis Nonnegative Matrix Factorization Document Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA

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