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Istituto di Scienza e Tecnologie dell'Informazione     
Lulli A., Debatty T., Dell'Amico M., Michiardi P., Ricci L. Scalable k-NN based text clustering. In: Big Data 2015 - IEEE International Conference on Big Data (Santa Clara, CA, USA, 29 October - 01 November 2015). Proceedings, pp. 958 - 963. IEEE, 2015.
 
 
Abstract
(English)
Clustering items using textual features is an important problem with many applications, such as root-cause analysis of spam campaigns, as well as identifying common topics in social media. Due to the sheer size of such data, algorithmic scalability becomes a major concern. In this work, we present our approach for text clustering that builds an approximate kNN graph, which is then used to compute connected components representing clusters. Our focus is to understand the scalability / accuracy tradeoff that underlies our method: we do so through an extensive experimental campaign, where we use real-life datasets, and show that even rough approximations of k-NN graphs are sufficient to identify valid clusters. Our method is scalable and can be easily tuned to meet requirements stemming from different application domains.
URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7363845
DOI: 10.1109/BigData.2015.7363845
Subject Clustering Algorithm
Distributed architectures
H.2 DATABASE MANAGEMENT


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