Title :
Scalable k-NN based text clustering
Author :
Alessandro Lulli;Thibault Debatty;Matteo Dell´Amico;Pietro Michiardi;Laura Ricci
Author_Institution :
University of Pisa, Italy
Abstract :
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 k-NN 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.
Keywords :
"Approximation algorithms","Clustering algorithms","Measurement","Approximation methods","Algorithm design and analysis","Scalability","Electronic mail"
Conference_Titel :
Big Data (Big Data), 2015 IEEE International Conference on
DOI :
10.1109/BigData.2015.7363845