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Combined Item Sets Generation using Modified Apriori Algorithm

Authors:

Minhajul Fahim Akas,

A. G. M. Zaman,

Abed KhanAuthors Info & Claims

ICCA 2020: Proceedings of the International Conference on Computing Advancements

Article No.: 70, Pages 1 - 3

https://doi.org/10.1145/3377049.3377125

Published: 20 March 2020 Publication History

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Abstract

Association rule mining is used to find association relationships among data sets. Apriori algorithm is one of the classical algorithms of association rule mining. It generates the association rules from transaction data, such as, if item 'a' is bought then what are the chances to buy item 'b'. It uses support and confidence values to generate the association rule.

In this paper, we modified the classical apriori algorithm in such way that so we can generate item sets as a package, which have higher possibility to buy together by the customers. To generate these packages, we introduced a new combined support value of the items sets. This combined support value is used along with the apriori algorithm to generate package items within a minimum support value. The generated item sets can also help the decision maker to forming new packages for the customers.

References

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R. Agrawal and R. Srikant, 1994 Fast algorithms for mining association rules, Proceedings of the 20th Very Large DataBases Conference (VLDB'94), Santiago de Chile, Chile, pp. 487--499.

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[2]

R. Karthiyayini and Dr. R. Balasubramanian, 2016 Affinity Analysis and Association Rule Mining using Apriori Algorithm in Market Basket Analysis, Volume 6, Issue 10, October 2016, ISSN: 2277 128X.

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Shaosong Yang and Guoyan Xu and Zhijian Wang and Fachao Zhou, 2015 The Parallel Improved Apriori Algorithm Research Based on Spark, 2015 Ninth International Conference on Frontier of Computer Science and Technology.

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Ketan D. Shah and Dr. (Mrs.) Sunita Mahajan, 2009 Maximizing the Efficiency of Parallel Apriori Algorithm, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

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Bo Wu and Defu Zhang and Qihua Lan and Jiemin Zheng, 2008, An Efficient Frequent Patterns Mininsg Algorithm based on Apriori Algorithm and the FPtree Structure, Third 2008 International Conference on Convergence and Hybrid Information Technology.

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Xueyan Lin, 2014 MR-Apriori: Association Rules Algorithm Based on MapReduce, 2014 5th IEEE International Conference on Software Engineering and Service Science (ICSESS).

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Cited By

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Challa SIqbal RLiu S(2023)An Unsupervised Learning Approach for Smart Home Operational Policy Generation2023 IEEE 20th Consumer Communications & Networking Conference (CCNC)10.1109/CCNC51644.2023.10059897(1-6)Online publication date: 8-Jan-2023
https://doi.org/10.1109/CCNC51644.2023.10059897
Rahman MArefin MRahman SAhmed AIslam TDhar PKwon O(2022)A Comprehensive Survey on Affinity Analysis, Bibliomining, and Technology Mining: Past, Present, and Future ResearchApplied Sciences10.3390/app1210522712:10(5227)Online publication date: 21-May-2022
https://doi.org/10.3390/app12105227
Liu QShi TRen L(2022)Device Action Prediction Based on K-means and Apriori for Smart Home2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)10.1109/IPEC54454.2022.9777395(1015-1021)Online publication date: 14-Apr-2022
https://doi.org/10.1109/IPEC54454.2022.9777395

Index Terms

Combined Item Sets Generation using Modified Apriori Algorithm
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Association rules

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ICCA 2020: Proceedings of the International Conference on Computing Advancements

January 2020

517 pages

ISBN:9781450377782

DOI:10.1145/3377049

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]

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Association for Computing Machinery

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Published: 20 March 2020

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ICCA 2020: International Conference on Computing Advancements

January 10 - 12, 2020

Dhaka, Bangladesh

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Challa SIqbal RLiu S(2023)An Unsupervised Learning Approach for Smart Home Operational Policy Generation2023 IEEE 20th Consumer Communications & Networking Conference (CCNC)10.1109/CCNC51644.2023.10059897(1-6)Online publication date: 8-Jan-2023
https://doi.org/10.1109/CCNC51644.2023.10059897
Rahman MArefin MRahman SAhmed AIslam TDhar PKwon O(2022)A Comprehensive Survey on Affinity Analysis, Bibliomining, and Technology Mining: Past, Present, and Future ResearchApplied Sciences10.3390/app1210522712:10(5227)Online publication date: 21-May-2022
https://doi.org/10.3390/app12105227
Liu QShi TRen L(2022)Device Action Prediction Based on K-means and Apriori for Smart Home2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)10.1109/IPEC54454.2022.9777395(1015-1021)Online publication date: 14-Apr-2022
https://doi.org/10.1109/IPEC54454.2022.9777395

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Research and improvement on association rule algorithm based on FP-Growth

Mining Association Rules Based on Apriori Algorithm and Application

Study on the Discovery Algorithm of the Frequent Item Sets

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Research and improvement on association rule algorithm based on FP-Growth

Mining Association Rules Based on Apriori Algorithm and Application

Study on the Discovery Algorithm of the Frequent Item Sets

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