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https://github.com/fars-data-analysis/FIS

Frequent Itemsets in Map-Reduce with Spark
https://github.com/fars-data-analysis/FIS

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Frequent Itemsets in Map-Reduce with Spark

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README 

        
# FIS (Frequent Item-Sets)



#### Big Data Project



**Students:**

+ [Andrea Galloni](http://www.andreagalloni.eu) (andrea [dot] galloni [at] studenti [dot] unitn [dot] it )

+ Daniel Bruzual (daniel [dot] bruzualbalzan [at] studenti [dot] unitn [dot] it )



**Organization:** [UniTN](http://www.unitn.it/en)



**Course:** [Big Data and Social Networks](http://web.unitn.it/scienze/25367/struttura-del-corso)



## 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](https://en.wikipedia.org/wiki/Algorithm):



+ [Apriori](https://en.wikipedia.org/wiki/Apriori_algorithm) [3]

+ [FP-Growth](https://en.wikipedia.org/wiki/Association_rule_learning#FP-growth_algorithm) [4]

+ SON [8]

+ BonGar (**novel solution**)



#### [DATA-SETS:](http://fimi.ua.ac.be/data/)



+ MushRooms

+ Retail

+ Kosarak

+ [GES (General Estimates System)](ftp://ftp.nhtsa.dot.gov/GES)



#### 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.



For more information, please refer to 



#.