Feature Selection and Document Clustering | Center for Big Data Analytics

Abstract: Feature selection is a basic step in the construction of a vector space or bag of words model [BB99]. In particular, when the processing task is to partition a given document collection into clusters of similar documents a choice of good features along with good clustering algorithms is of paramount importance. This chapter suggests two techniques for feature or term selection along with a number of clustering strategies. The selection techniques significantly reduce the dimension of the vector space model. Examples that illustrate the effectiveness of the proposed algorithms are provided.

Topics:
Data Clustering

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Citation

Feature Selection and Document Clustering (pdf, software)
I. Dhillon, J. Kogan, M. Nicholas.
A Comprehensive Survey of Text Mining, pp. 73-100, January 2003.

Bibtex:
@book{dhillon2003featurese, author = "Inderjit S. Dhillon AND J. Kogan AND M. Nicholas", title = "Feature Selection and Document Clustering", booktitle = "A Comprehensive Survey of Text Mining", publisher = "Springer-Verlag", publisher = "Springer-Verlag", page = "73–100", year = "2003", month = "jan", abstract = "Feature selection is a basic step in the construction of a vector space or bag of words model [BB99]. In particular, when the processing task is to partition a given document collection into clusters of similar documents a choice of good features along with good clustering algorithms is of paramount importance. This chapter suggests two techniques for feature or term selection along with a number of clustering strategies. The selection techniques significantly reduce the dimension of the vector space model. Examples that illustrate the effectiveness of the proposed algorithms are provided." }