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Mining Frequent Itemsets Using Improved Apriori on Spark

Published: 06 April 2019 Publication History

Abstract

Finding the frequent itemset is one of the most investigated extents of data mining. The Apriori algorithm is the most established algorithm for frequent itemset mining, but it has issues regarding scanning frequent databases and generating a large amount of candidate sets. To solve these issues, an Improved Apriori algorithm was proposed. We examined the data structure, implementation, and algorithmic features that mainly focus on frequent itemset mining. We are representing an Improved Apriori algorithm on Spark in which simple and scalable implementation is done to achieve a faster process with lower support thresholds. We examined the improved Apriori algorithm on Extended Bakery Dataset and Retail Dataset. The results show execution time was reduced by 40% and 57% compared with the original Apriori algorithm.

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  • (2024)A Survey on Algorithms and Software for the Frequent Itemset Hiding Problem2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA62523.2024.10786657(1-8)Online publication date: 17-Jul-2024
  • (2024)An End-to-End Knowledge Graph Solution to the Frequent Itemset Hiding ProblemInformation Sciences10.1016/j.ins.2024.120680(120680)Online publication date: May-2024
  • (2022)Scratch-DKG: A Framework for Constructing Scratch Domain Knowledge GraphIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2020.299671010:1(170-185)Online publication date: 1-Jan-2022
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    cover image ACM Other conferences
    ICISDM '19: Proceedings of the 2019 3rd International Conference on Information System and Data Mining
    April 2019
    251 pages
    ISBN:9781450366359
    DOI:10.1145/3325917
    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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    Published: 06 April 2019

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    1. Apriori Algorithm
    2. association rule

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    View all
    • (2024)A Survey on Algorithms and Software for the Frequent Itemset Hiding Problem2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA62523.2024.10786657(1-8)Online publication date: 17-Jul-2024
    • (2024)An End-to-End Knowledge Graph Solution to the Frequent Itemset Hiding ProblemInformation Sciences10.1016/j.ins.2024.120680(120680)Online publication date: May-2024
    • (2022)Scratch-DKG: A Framework for Constructing Scratch Domain Knowledge GraphIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2020.299671010:1(170-185)Online publication date: 1-Jan-2022
    • (2022)Performing in-situ analyticsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105480116:COnline publication date: 1-Nov-2022
    • (2021)An Approach to Improve Apriori Algorithm for Extraction of Frequent Itemsets2021 7th International Conference on Web Research (ICWR)10.1109/ICWR51868.2021.9443137(206-210)Online publication date: 19-May-2021
    • (2021)Frequent itemset hiding revisited: pushing hiding constraints into miningApplied Intelligence10.1007/s10489-021-02490-452:3(2539-2555)Online publication date: 16-Jun-2021
    • (2021)A Database Reconstruction Approach for the Inverse Frequent Itemset Mining ProblemAdvances in Artificial Intelligence-based Technologies10.1007/978-3-030-80571-5_4(45-58)Online publication date: 3-Oct-2021
    • (2020)Review of Apriori based Frequent Itemset Mining Solutions on Big Data2020 6th International Conference on Web Research (ICWR)10.1109/ICWR49608.2020.9122295(157-164)Online publication date: Apr-2020
    • (2020)Parallel based Hiding of Sensitive Knowledge2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00188(1249-1254)Online publication date: Nov-2020

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