Comparative analysis of different versions of association rule mining algorithm on AWS-EC2

Ahamed Lebbe Sayeth Saabith, Elankovan A Sundararajan, Azuraliza Abu Bakar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Data mining is an essential step of knowledge discovery in databases (KDD) process by analyzing the huge amount of data from different perspectives and summarizing it into potentially valuable, valid, novel, interesting, and previously unknown information. Due to the importance of extracting knowledge from the massive data repositories, data mining is an essential components in various fields. Association rule mining (ARM), is one of the most important and well researched techniques of data mining, It aims to extract essential relationships, frequent patterns, associations among itemsets in the transaction databases or other data repositories. Many algorithm have been proposed to find the frequent itemset efficiently. In this research, we have chosen four well established frequent itemset mining methods which are Apriori, Apriori TID, Eclat, and FP-Growth to analyze their performance on cloud environment. Cloud computing is a new paradigm to analyze big data efficiently and cost effectively. In this study we analyzed the algorithms on Amazon web service (AWS) platform using elastic cloud computing (EC2) service. We thereafter compare the four algorithms based on their execution time by varying the minimum support (min_sup) values.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages64-76
Number of pages13
Volume9429
ISBN (Print)9783319259383, 9783319259383
DOIs
Publication statusPublished - 2015
Event4th International Visual Informatics Conference, IVIC 2015 - Bangi, Malaysia
Duration: 17 Nov 201519 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9429
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th International Visual Informatics Conference, IVIC 2015
CountryMalaysia
CityBangi
Period17/11/1519/11/15

Fingerprint

Association Rule Mining
Association rules
Comparative Analysis
Web services
Web Services
Data mining
Data Mining
Frequent Itemsets
Cloud computing
Cloud Computing
Repository
Knowledge Discovery in Databases
Frequent Pattern
Essential Component
Execution Time
Transactions
Mining
Paradigm
Valid
Unknown

Keywords

  • ARM
  • AWS-EC2
  • Cloud computing
  • Data mining
  • KDD

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Saabith, A. L. S., A Sundararajan, E., & Abu Bakar, A. (2015). Comparative analysis of different versions of association rule mining algorithm on AWS-EC2. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9429, pp. 64-76). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9429). Springer Verlag. https://doi.org/10.1007/978-3-319-25939-0_6

Comparative analysis of different versions of association rule mining algorithm on AWS-EC2. / Saabith, Ahamed Lebbe Sayeth; A Sundararajan, Elankovan; Abu Bakar, Azuraliza.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9429 Springer Verlag, 2015. p. 64-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9429).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Saabith, ALS, A Sundararajan, E & Abu Bakar, A 2015, Comparative analysis of different versions of association rule mining algorithm on AWS-EC2. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9429, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9429, Springer Verlag, pp. 64-76, 4th International Visual Informatics Conference, IVIC 2015, Bangi, Malaysia, 17/11/15. https://doi.org/10.1007/978-3-319-25939-0_6
Saabith ALS, A Sundararajan E, Abu Bakar A. Comparative analysis of different versions of association rule mining algorithm on AWS-EC2. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9429. Springer Verlag. 2015. p. 64-76. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-25939-0_6
Saabith, Ahamed Lebbe Sayeth ; A Sundararajan, Elankovan ; Abu Bakar, Azuraliza. / Comparative analysis of different versions of association rule mining algorithm on AWS-EC2. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9429 Springer Verlag, 2015. pp. 64-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{baf233ba61cd45b28902b7f85d126ba7,
title = "Comparative analysis of different versions of association rule mining algorithm on AWS-EC2",
abstract = "Data mining is an essential step of knowledge discovery in databases (KDD) process by analyzing the huge amount of data from different perspectives and summarizing it into potentially valuable, valid, novel, interesting, and previously unknown information. Due to the importance of extracting knowledge from the massive data repositories, data mining is an essential components in various fields. Association rule mining (ARM), is one of the most important and well researched techniques of data mining, It aims to extract essential relationships, frequent patterns, associations among itemsets in the transaction databases or other data repositories. Many algorithm have been proposed to find the frequent itemset efficiently. In this research, we have chosen four well established frequent itemset mining methods which are Apriori, Apriori TID, Eclat, and FP-Growth to analyze their performance on cloud environment. Cloud computing is a new paradigm to analyze big data efficiently and cost effectively. In this study we analyzed the algorithms on Amazon web service (AWS) platform using elastic cloud computing (EC2) service. We thereafter compare the four algorithms based on their execution time by varying the minimum support (min_sup) values.",
keywords = "ARM, AWS-EC2, Cloud computing, Data mining, KDD",
author = "Saabith, {Ahamed Lebbe Sayeth} and {A Sundararajan}, Elankovan and {Abu Bakar}, Azuraliza",
year = "2015",
doi = "10.1007/978-3-319-25939-0_6",
language = "English",
isbn = "9783319259383",
volume = "9429",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "64--76",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Comparative analysis of different versions of association rule mining algorithm on AWS-EC2

AU - Saabith, Ahamed Lebbe Sayeth

AU - A Sundararajan, Elankovan

AU - Abu Bakar, Azuraliza

PY - 2015

Y1 - 2015

N2 - Data mining is an essential step of knowledge discovery in databases (KDD) process by analyzing the huge amount of data from different perspectives and summarizing it into potentially valuable, valid, novel, interesting, and previously unknown information. Due to the importance of extracting knowledge from the massive data repositories, data mining is an essential components in various fields. Association rule mining (ARM), is one of the most important and well researched techniques of data mining, It aims to extract essential relationships, frequent patterns, associations among itemsets in the transaction databases or other data repositories. Many algorithm have been proposed to find the frequent itemset efficiently. In this research, we have chosen four well established frequent itemset mining methods which are Apriori, Apriori TID, Eclat, and FP-Growth to analyze their performance on cloud environment. Cloud computing is a new paradigm to analyze big data efficiently and cost effectively. In this study we analyzed the algorithms on Amazon web service (AWS) platform using elastic cloud computing (EC2) service. We thereafter compare the four algorithms based on their execution time by varying the minimum support (min_sup) values.

AB - Data mining is an essential step of knowledge discovery in databases (KDD) process by analyzing the huge amount of data from different perspectives and summarizing it into potentially valuable, valid, novel, interesting, and previously unknown information. Due to the importance of extracting knowledge from the massive data repositories, data mining is an essential components in various fields. Association rule mining (ARM), is one of the most important and well researched techniques of data mining, It aims to extract essential relationships, frequent patterns, associations among itemsets in the transaction databases or other data repositories. Many algorithm have been proposed to find the frequent itemset efficiently. In this research, we have chosen four well established frequent itemset mining methods which are Apriori, Apriori TID, Eclat, and FP-Growth to analyze their performance on cloud environment. Cloud computing is a new paradigm to analyze big data efficiently and cost effectively. In this study we analyzed the algorithms on Amazon web service (AWS) platform using elastic cloud computing (EC2) service. We thereafter compare the four algorithms based on their execution time by varying the minimum support (min_sup) values.

KW - ARM

KW - AWS-EC2

KW - Cloud computing

KW - Data mining

KW - KDD

UR - http://www.scopus.com/inward/record.url?scp=84950135184&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84950135184&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-25939-0_6

DO - 10.1007/978-3-319-25939-0_6

M3 - Conference contribution

SN - 9783319259383

SN - 9783319259383

VL - 9429

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 64

EP - 76

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -