skip to main content
10.1145/3279996.3279999acmotherconferencesArticle/Chapter ViewAbstractPublication PagesdatasConference Proceedingsconference-collections
research-article

Data distribution method for scalable actionable pattern mining

Published: 01 October 2018 Publication History

Editorial Notes

NOTICE OF CONCERN: ACM has received evidence that casts doubt on the integrity of the peer review process for the DATA 2018 Conference. As a result, ACM is issuing a Notice of Concern for all papers published and strongly suggests that the papers from this Conference not be cited in the literature until ACM's investigation has concluded and final decisions have been made regarding the integrity of the peer review process for this Conference.

Abstract

Action Rules are rule based systems for discovering actionable patterns hidden in a large dataset. Action Rules recommend actions that a user or a system can undertake to their advantage, or to accomplish their goal. Current Action Rules extraction methods are unable to process huge volumes of data in a reasonable time and it requires a distributed and parallel extraction methods. Limited research has been done on extracting Action Rules using a distributed scenario. Major complications of discovering Action Rules with such distributed systems are data distribution among computing nodes and calculation of major parameters of action rules. In this work, we propose few methods to handle the big data distribution among computation nodes using the Spark framework. With enhanced experiments made on datasets in transportation, medical, and business domains, we show our methods achieve almost equal valid results compared to results from classical non-distributed Action Rule discovery methods with improved run time.

References

[1]
S. Ramachandran A.A. Tzacheva, C.C. Sankar and R.A. Shankar. 2016. Support Confidence and Utility of Action Rules Triggered by Meta-Actions. In proceedings of 2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA 2016). IEEE Computer Society.
[2]
Rakesh Agrawal, Ramakrishnan Srikant, et al. 1994. Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB, Vol. 1215. 487--499.
[3]
Mamoun Al-Mardini, Ayman Hajja, Lina Clover, David Olaleye, Youngjin Park, Jay Paulson, and Yang Xiao. 2016. Reduction of Hospital Readmissions through Clustering Based Actionable Knowledge Mining. In Web Intelligence (WI), 2016 IEEE/WIC/ACM International Conference on. IEEE, 444--448.
[4]
Mamoun Almardini, Ayman Hajja, Zbigniew W Raś, Lina Clover, David Olaleye, Youngjin Park, Jay Paulson, and Yang Xiao. 2015. Reduction of readmissions to hospitals based on actionable knowledge discovery and personalization. In Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. Springer, 39--55.
[5]
A. Bagavathi, P. Mummoju, K. Tarnowska, A. A. Tzacheva, and Z. W. Ras. 2017. SARGS method for distributed actionable pattern mining using spark. In 2017 IEEE International Conference on Big Data (Big Data). 4272--4281.
[6]
Jeffrey Dean and Sanjay Ghemawat. 2008. MapReduce: simplified data processing on large clusters. Commun. ACM 51, 1 (2008), 107--113.
[7]
Seunghyun Im and Zbigniew W Raś. 2008. Action rule extraction from a decision table: ARED. In International Symposium on Methodologies for Intelligent Systems. Springer, 160--168.
[8]
S.R. Marepally J.W. Grzymala-Busse and Y. Yao. 2013. An Empirical Comparison of Rule Sets Induced by LERS and Probabilistic Rough Classification. In Rough Sets and Intelligent Systems. Vol. 1. Springer, 261--276.
[9]
Jieyan Kuang, Albert Daniel, Jill Johnston, and Zbigniew W Raś. 2014. Hierarchically structured recommender system for improving NPS of a company. In International Conference on Rough Sets and Current Trends in Computing. Springer, 347--357.
[10]
M. Lichman. 2013. UCI Machine Learning Repository. Technical Report. Irvine, CA, USA.
[11]
Ming-Yen Lin, Pei-Yu Lee, and Sue-Chen Hsueh. 2012. Apriori-based frequent itemset mining algorithms on MapReduce. In Proceedings of the 6th international conference on ubiquitous information management and communication. ACM, 76.
[12]
Maria Malek and Hubert Kadima. 2013. Searching frequent itemsets by clustering data: Towards a parallel approach using mapreduce. In Web Information Systems Engineering-WISE 2011 and 2012 Workshops. Springer, 251--258.
[13]
Hongjian Qiu, Rong Gu, Chunfeng Yuan, and Yihua Huang. 2014. Yafim: a parallel frequent itemset mining algorithm with spark. In Parallel & Distributed Processing Symposium Workshops (IPDPSW), 2014 IEEE International. IEEE, 1664--1671.
[14]
Zbigniew W Ras, Agnieszka Dardzinska, Li-Shiang Tsay, and Hanna Wasyluk. 2008. Association action rules. In Data Mining Workshops, 2008. ICDMW'08. IEEE International Conference on. IEEE, 283--290.
[15]
Zbigniew W Ras, Katarzyna A Tarnowska, Jieyan Kuang, Lynn Daniel, and Doug Fowler. 2017. User Friendly NPS-Based Recommender System for Driving Business Revenue. In International Joint Conference on Rough Sets. Springer, 34--48.
[16]
Zbigniew W Ras and Alicja Wieczorkowska. 2000. Action-Rules: How to increase profit of a company. In European Conference on Principles of Data Mining and Knowledge Discovery. Springer, 587--592.
[17]
Zbigniew W Raś, Elżbieta Wyrzykowska, and Hanna Wasyluk. 2007. ARAS: Action rules discovery based on agglomerative strategy. In International Workshop on Mining Complex Data. Springer, 196--208.
[18]
Sanjay Rathee, Manohar Kaul, and Arti Kashyap. 2015. R-Apriori: an efficient apriori based algorithm on spark. In Proceedings of the 8th Workshop on Ph. D. Workshop in Information and Knowledge Management. ACM, 27--34.
[19]
Matteo Riondato, Justin A DeBrabant, Rodrigo Fonseca, and Eli Upfal. 2012. PARMA: a parallel randomized algorithm for approximate association rules mining in MapReduce. In Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 85--94.
[20]
Katarzyna A Tarnowska, Zbigniew W Ras, and Pawel J Jastreboff. 2017. Mining for Actionable Knowledge in Tinnitus Datasets. In Thriving Rough Sets. Springer, 367--395.
[21]
Li-Shiang Tsay* and Zbigniew W Raś. 2005. Action rules discovery: system DEAR2, method and experiments. Journal of Experimental & Theoretical Artificial Intelligence 17, 1--2 (2005), 119--128.
[22]
Angelina A Tzacheva and Zbigniew W Ras. 2010. Association action rules and action paths triggered by meta-actions. In Granular Computing (GrC), 2010 IEEE International Conference on. IEEE, 772--776.
[23]
Le Wang, Lin Feng, Jing Zhang, and Pengyu Liao. 2014. An efficient algorithm of frequent itemsets mining based on mapreduce. JOURNAL OF INFORMATION & COMPUTATIONAL SCIENCE 11, 8 (2014), 2809--2816.
[24]
Xian Wu, Wei Fan, Jing Peng, Kun Zhang, and Yong Yu. 2015. Iterative sampling based frequent itemset mining for big data. International Journal of Machine Learning and Cybernetics 6, 6 (2015), 875--882.
[25]
Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott Shenker, and Ion Stoica. 2012. Resilient Distributed Datasets: A Fault-tolerant Abstraction for In-memory Cluster Computing. In Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation (NSDI'12). USENIX Association, Berkeley, CA, USA, 2--2. htttp://dl.acm.org/citation.cfm?id=2228298.2228301

Cited By

View all
  • (2019)Scalable Action Mining for Recommendations to Reduce Hospital Readmission2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)10.1109/IRI.2019.00036(159-166)Online publication date: Jul-2019
  • (2019)Emotion Mining in Social Media DataProcedia Computer Science10.1016/j.procs.2019.09.160159:C(58-66)Online publication date: 1-Jan-2019

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
DATA '18: Proceedings of the First International Conference on Data Science, E-learning and Information Systems
October 2018
274 pages
ISBN:9781450365369
DOI:10.1145/3279996
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2018

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. action rules
  2. data distribution
  3. distributed processing

Qualifiers

  • Research-article

Conference

DATA '18

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)3
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2019)Scalable Action Mining for Recommendations to Reduce Hospital Readmission2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)10.1109/IRI.2019.00036(159-166)Online publication date: Jul-2019
  • (2019)Emotion Mining in Social Media DataProcedia Computer Science10.1016/j.procs.2019.09.160159:C(58-66)Online publication date: 1-Jan-2019

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media