Ecole d'ingénieur et centre de recherche en Sciences du numérique

Scalable k-NN based text clustering

Lulli, Alessandro; Debatty, Thibault; Dell'Amico, Matteo; Michiardi, Pietro; Ricci, Laura

BIGDATA 2015, IEEE International Conference on Big Data, October 29-November 1, 2015, Santa Clara, USA

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.

Document Doi Hal Bibtex

Titre:Scalable k-NN based text clustering
Type:Conférence
Langue:English
Ville:Santa Clara
Pays:ÉTATS-UNIS
Date:
Département:Data Science
Eurecom ref:4743
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Bibtex: @inproceedings{EURECOM+4743, doi = {http://dx.doi.org/10.1109/BigData.2015.7363845}, year = {2015}, title = {{S}calable k-{NN} based text clustering}, author = {{L}ulli, {A}lessandro and {D}ebatty, {T}hibault and {D}ell'{A}mico, {M}atteo and {M}ichiardi, {P}ietro and {R}icci, {L}aura}, booktitle = {{BIGDATA} 2015, {IEEE} {I}nternational {C}onference on {B}ig {D}ata, {O}ctober 29-{N}ovember 1, 2015, {S}anta {C}lara, {USA}}, address = {{S}anta {C}lara, {\'{E}}{TATS}-{UNIS}}, month = {10}, url = {http://www.eurecom.fr/publication/4743} }
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