Aniket Rangrej
ABSTRACT
We compare various document clustering techniques including K-means, SVD-based method and a graph-based approach and their performance on short text data collected from Twitter. We define a measure for evaluating the cluster error with these techniques. Observations show that graph-based approach using affinity propagation performs best in clustering short text data with minimal cluster error.
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Digital Library
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- Jeon hyung Kang, Kristina Lerman, and Plangprasopchok Anon, Analyzing microblogs with affinity propagation, Proceedings of KDD workshop on Social Media Analytic, July 2010. Google ScholarDigital Library
- Brendan O'Connor, Michel Krieger, and David Ahn, Tweetmotif: Exploratory search and topic summarization for twitter, ICWSM, 2010.Google Scholar
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Index Terms
- Comparative study of clustering techniques for short text documents
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