Presentation given at 2014 IEEE International Conference on Big Data, 28 October 2014

1. IEEE BigData 2014
Building k-nn Graphs From
Large Text Data
Thibault Debatty, Pietro Michiardi,
Olivier Thonnard & Wim Mees

2. The context : TRIAGE
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3. The problem
The subject of a SPAM is more than a
set of keywords
Rep|icaWatches For Sale: cRolex
Rep1icaWatches For Sale: R0lex
RepilcaWatches For Sale: Rolex
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4. The problem
How to build a k-nn graph from
large text data using using
arbitrary similarity metric?
– Naive
– Index
– Locality-sensitive hashing (LSH)
– nn-descent
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5. NNCTPH
Map
Reduce
SPAM 1 SPAM 2
CTPH* CTPH*
Sig 1 Sig 2
nn-descent
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6. Experimental results
● Dataset: 200k to 800k spam subjects
● Tests:
– Stages
– Buckets
– Comparison with MR nn-descent
– Scalability
● Measures:
– Speed
– Recall
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7. Experimental results : stages
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8. Experimental results : buckets
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9. Experimental results : nn-descent
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10. Experimental results : scalability
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11. Conclusions & future work...
● 10x faster than MR nn-descent
● Speedup increases with size of dataset
● Limited recall
● Future:
– Improve recall?
– Quality of graph?
– Influence of graph quality?
– Compare with bag-of-words model
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12. Thank you!
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