TAPNN was incorporated in the latest version of the L2AP program. This program implements several methods for solving the AllPairs Similarity Search problem for cosine similarity and Tanimoto coefficient, including AllPairs [3], MMJoin [4], MK-Join[5-7], IdxJoin [1], L2AP [1] and TAPNN[2]. Details for the methods can be found in [1] and [2].
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README
Contact me via email if you need additional information or find any bugs: david [period1] anastasiu [atSign] sjsu [period2] edu.
Please cite the following paper if you make use of this program or any of its components in your research.

David C. Anastasiu and George Karypis. Efficient Identification of Tanimoto Nearest Neighbors. Proceedings of the 3rd IEEE International Conference on Data Science and Advanced Analytics (DSAA 2016). 

@inproceedings{anastasiu2016,
    author = {Anastasiu, David C. and Karypis, George},
    title = {Efficient Identification of Tanimoto Nearest Neighbors},
    booktitle = {3rd IEEE International Conference on Data Science and Advanced Analytics},
    series = {DSAA '16},
    year = {2016},
    location = {Montr\'{e}al, Canada},
    numpages = {10},
}

References:

[1] David C. Anastasiu and George Karypis. L2AP: Fast Cosine Similarity Search With Prefix L-2 Norm Bounds. Proceedings of the 30th IEEE International Conference on Data Engineering (ICDE 2014).
[2] David C. Anastasiu and George Karypis. Efficient Identification of Tanimoto Nearest Neighbors. Proceedings of the 3rd IEEE International Conference on Data Science and Advanced Analytics (DSAA 2016).
[3] Roberto J. Bayardo, Yiming Ma, and Ramakrishnan Srikant. 2007. Scaling up all pairs similarity search. In Proceedings of the 16th international conference on World Wide Web (WWW '07). ACM, New York, NY, USA, 131-140.
[4] Dongjoo Lee, Jaehui Park, Junho Shim, and Sang-goo Lee. 2010. An efficient similarity join algorithm with cosine similarity predicate. In Proceedings of the 21st international conference on Database and expert systems applications: Part II (DEXA'10), Pablo Garcia Bringas, Abdelkader Hameurlain, and Gerald Quirchmayr (Eds.). Springer-Verlag, Berlin, Heidelberg, 422-436
[5] M. Kryszkiewicz. Bounds on lengths of real valued vectors similar with regard to the tanimoto similarity. Intelligent Information and Database Systems, ser. Lecture Notes in Computer Science, A. Selamat, N. Nguyen, and H. Haron, Eds. Springer Berlin Heidelberg, 2013, vol. 7802, pp. 445-454.
[6] ----. Using non-zero dimensions for the cosine and tanimoto similarity search among real valued vectors. Fundamenta Informaticae, vol. 127, no. 1-4, pp. 307-323, 2013.
[7] ----. Using non-zero dimensions and lengths of vectors for the tanimoto similarity search among real valued vectors. Intelligent Information and Database Systems. Springer International Publishing, 2014, pp. 173-182.
[8] Venu Satuluri and Srinivasan Parthasarathy. 2012. Bayesian locality sensitive hashing for fast similarity search. Proc. VLDB Endow. 5, 5 (January 2012), 430-441.