Survey of Text Mining: Clustering, Classification, and Retrieval

By Michael W. Berry, Malú Castellanos. 2007

535 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6c0b6846290636dbcd66e9ee3eed2aee1b164261

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Dan Zhang
2. Luo Si
3. Jingrui He
4. Saeed Abu-Nimeh
5. Suku Nair
6. Richard D. Lawrence
7. Dario Nappa
8. Xinlei Wang

Citations in the corpus

• MILEAGE: Multiple Instance LEArning with Global Embedding (2013, methodology): “For each of the sub-dataset, after removing the stop words and stemming, tf-idf (Manning et al., 2008) features are extracted and processed by PCA (Berry & Castellanos, 2007).”
• Text Mining Infrastructure in R (2008, unknown): “Term-document matrices Finally we have a class for term-document matrices (Berry 2003; Shawe-Taylor and Cristianini 2004), probably the most common way of representing texts for further computation.”
• A comparison of machine learning techniques for phishing detection (2007, background): “For example, “suspended” and “suspending” are converted to their origin “suspend” [4].”