Scalable Graph Building from Text Data

Thibault Debatty, Pietro Michiardi, Olivier Thonnard, Wim Mees
Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, PMLR 36:120-132, 2014.

Abstract

In this paper we propose NNCTPH, a new MapReduce algorithm that is able to build an approximate k-NN graph from large text datasets. The algorithm uses a modified version of Context Triggered Piecewise Hashing to bin the input data into buckets, and uses an exhaustive search inside the buckets to build the graph. It also uses multiple stages to join the different unconnected subgraphs. We experimentally test the algorithm on different datasets consisting of the subject of spam emails. Although the algorithm is still at an early development stage, it already proves to be four times faster than a MapReduce implementation of NN-Descent, for the same quality of produced graph.

Cite this Paper

BibTeX
@InProceedings{pmlr-v36-debatty14, title = {Scalable Graph Building from Text Data}, author = {Debatty, Thibault and Michiardi, Pietro and Thonnard, Olivier and Mees, Wim}, booktitle = {Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications}, pages = {120--132}, year = {2014}, editor = {Fan, Wei and Bifet, Albert and Yang, Qiang and Yu, Philip S.}, volume = {36}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {24 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v36/debatty14.pdf}, url = {https://proceedings.mlr.press/v36/debatty14.html}, abstract = {In this paper we propose NNCTPH, a new MapReduce algorithm that is able to build an approximate k-NN graph from large text datasets. The algorithm uses a modified version of Context Triggered Piecewise Hashing to bin the input data into buckets, and uses an exhaustive search inside the buckets to build the graph. It also uses multiple stages to join the different unconnected subgraphs. We experimentally test the algorithm on different datasets consisting of the subject of spam emails. Although the algorithm is still at an early development stage, it already proves to be four times faster than a MapReduce implementation of NN-Descent, for the same quality of produced graph.} }
Endnote
%0 Conference Paper %T Scalable Graph Building from Text Data %A Thibault Debatty %A Pietro Michiardi %A Olivier Thonnard %A Wim Mees %B Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications %C Proceedings of Machine Learning Research %D 2014 %E Wei Fan %E Albert Bifet %E Qiang Yang %E Philip S. Yu %F pmlr-v36-debatty14 %I PMLR %P 120--132 %U https://proceedings.mlr.press/v36/debatty14.html %V 36 %X In this paper we propose NNCTPH, a new MapReduce algorithm that is able to build an approximate k-NN graph from large text datasets. The algorithm uses a modified version of Context Triggered Piecewise Hashing to bin the input data into buckets, and uses an exhaustive search inside the buckets to build the graph. It also uses multiple stages to join the different unconnected subgraphs. We experimentally test the algorithm on different datasets consisting of the subject of spam emails. Although the algorithm is still at an early development stage, it already proves to be four times faster than a MapReduce implementation of NN-Descent, for the same quality of produced graph.
RIS
TY - CPAPER TI - Scalable Graph Building from Text Data AU - Thibault Debatty AU - Pietro Michiardi AU - Olivier Thonnard AU - Wim Mees BT - Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications DA - 2014/08/13 ED - Wei Fan ED - Albert Bifet ED - Qiang Yang ED - Philip S. Yu ID - pmlr-v36-debatty14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 36 SP - 120 EP - 132 L1 - http://proceedings.mlr.press/v36/debatty14.pdf UR - https://proceedings.mlr.press/v36/debatty14.html AB - In this paper we propose NNCTPH, a new MapReduce algorithm that is able to build an approximate k-NN graph from large text datasets. The algorithm uses a modified version of Context Triggered Piecewise Hashing to bin the input data into buckets, and uses an exhaustive search inside the buckets to build the graph. It also uses multiple stages to join the different unconnected subgraphs. We experimentally test the algorithm on different datasets consisting of the subject of spam emails. Although the algorithm is still at an early development stage, it already proves to be four times faster than a MapReduce implementation of NN-Descent, for the same quality of produced graph. ER -
APA
Debatty, T., Michiardi, P., Thonnard, O. & Mees, W.. (2014). Scalable Graph Building from Text Data. Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, in Proceedings of Machine Learning Research 36:120-132 Available from https://proceedings.mlr.press/v36/debatty14.html.

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