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
Ville:Santa Clara
Département:Data Science
Eurecom ref:4743
Copyright: © 2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Bibtex: @inproceedings{EURECOM+4743, doi = {}, 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 = {} }
Voir aussi: