FIS (Frequent Item-Sets)

Big Data Project

Students:

Andrea Galloni (andrea [dot] galloni [at] studenti [dot] unitn [dot] it )
Daniel Bruzual (daniel [dot] bruzualbalzan [at] studenti [dot] unitn [dot] it )

Organization: UniTN

Course: Big Data and Social Networks

Frequent Itemsets in Map-Reduce with Spark

This document presents the work done for the Big Data and Social Networks course taken at UNITN during the first semester of 2015-2016. The project consisted of several steps.

First a dataset from traffic accidents in the USA NASS General Estimates System was selected and integrated to ensure consistency across different years. This involved studying the data, reading the description manuals, building a common schema and then converting the data with the help of special data integration software Talend Studio

Afterwards, several known algorithms to calculate frequent itemsets were implemented. Apache Spark was used for implementing them in a parallel paradigm. A new parallel algorithm was also developed, which uses repeated random sampling and concepts from other algorithms to calculate frequent itemsets on big datasets while reducing execution time.

Finally, the algorithms were tested and compared using the integrated datasets, along with other classic datasets in the field of itemsets mining.

*For further details please have a look to the project report .pdf file. Any question? Please do not be shy write us an e-mail. *

ALGORITHMS:

Apriori [3]
FP-Growth [4]
SON [8]
BonGar (novel solution)

DATA-SETS:

MushRooms
Retail
Kosarak
GES (General Estimates System)

TESTING ENVIRONMENT:

OperativeSystem: OS X YOSEMITE 10.10.4 Spark: 1.6.0 Scala: 2.11.7 Python: 2.7.6 Memory: 8 GB DDR3 Processor INTEL CORE I5 2.6 GHz Number of Processors: 1 Total Number of Cores: 2 L2 Cache (per Core): 256 KB L3 Cache: 3 MB

REFERENCES:

[1] R. C. Agarwal, C. C. Aggarwal, and V. Prasad. A tree projection algorithm for generation of frequent item sets. Journal of parallel and Distributed Computing, 61(3):350–371, 2001.

[2] R. Agrawal, T. Imieli´nski, and A. Swami. Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2):207–216, 1993.

[3] R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases, VLDB ’94, pages 487–499, San Francisco, CA, USA, 1994. Morgan Kaufmann Publishers Inc.

[4] J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In ACM SIGMOD Record, volume 29, pages 1–12. ACM, 2000.

[5] M. Hontsma and A. Swami. Set oriented mining for association rules in relatrend database. Technical report, The technical report RJ9567, IBM Almaden Research Centre, San Jose, California, 1993.

[6] H. Li, Y. Wang, D. Zhang, M. Zhang, and E. Y. Chang. Pfp: parallel fp-growth for query recommendation. In Proceedings of the 2008 ACM conference on Recommender systems, pages 107–114. ACM, 2008.

[7] J. Malviya, A. Singh, and D. Singh. An fp tree based approach for extracting frequent pattern from large database by applying parallel and partition projection. International Journal of Computer Applications, 114(18), 2015.

[8] A. Savasere, E. R. Omiecinski, and S. B. Navathe. An efficient algorithm for mining association rules in large databases. 1995.

[9] H. Toivonen et al. Sampling large databases for association rules. In VLDB, volume 96, pages 134–145, 1996.

[10] R. Ullman and Leskovec. Mining of Massive Datasets. Cambridge University Press, Cambridge, 2014.

[11] N. U.S. Department of Transportation. National automotive sampling system (nass) general estimates system (ges). analytical user’s manual 1988-2014. November 2015.

[12] T. Xiao, C. Yuan, and Y. Huang. Pson: A parallelized son algorithm with mapreduce for mining frequent sets. In Parallel Architectures, Algorithms and Programming (PAAP), 2011 Fourth International Symposium on, pages 252–257. IEEE, 2011.

Technologies:

LICENSE:

This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or distribute this software, either in source code form or as a compiled binary, for any purpose, commercial or non-commercial, and by any means.

In jurisdictions that recognize copyright laws, the author or authors of this software dedicate any and all copyright interest in the software to the public domain. We make this dedication for the benefit of the public at large and to the detriment of our heirs and successors. We intend this dedication to be an overt act of relinquishment in perpetuity of all present and future rights to this software under copyright law.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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FIS

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