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Mining of High-Utility Itemsets by ACO Algorithm

Published: 15 August 2016 Publication History

Abstract

High-utility itemset mining (HUIM) is a major contemporary data mining issue. It is different from frequent itemset mining (FIM), which only considers the quantity factor. HUIM applies both the quantity and profit factors to be used to reveal the most profitable products. In this paper, a novel algorithm based on a evolutionary computation technique, ant colony optimization (ACO), is proposed to resolve this issue. Unlike genetic algorithms (GAs) and particle swarm optimizations (PSOs), ACOs produce a feasible solution in a constructive way. They can avoid generating unreasonable solutions as much as possible. Thus, a well-defined ACO approach can always obtain suitable solutions efficiently. An ant colony system (ACS), which is extended from ACO, is proposed to efficiently find HUIs (HUIM-ACS). In general, an EC algorithm cannot make sure the provided solution is the global optimal solution. But the designed HUIM-ACS algorithm maps the completed solution space into the routing graph. Therefore, it guarantees that it obtains all of the HUIs when there is no candidate edge from the starting point. In addition, HUIM-ACS does not estimate the same feasible solution again in its process in order to avoidwasting computational resource. Substantial experiments on real-life datasets show that the proposed algorithm outperforms the other heuristic algorithms for mining HUIs in terms of the number of discovered HUIs, and convergence.

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Cited By

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  • (2022)An overview of high utility itemsets mining methods based on intelligent optimization algorithmsKnowledge and Information Systems10.1007/s10115-022-01741-164:11(2945-2984)Online publication date: 3-Sep-2022
  • (2022)A survey on soft computing-based high-utility itemsets miningSoft Computing10.1007/s00500-021-06613-426:13(6347-6392)Online publication date: 25-Jan-2022
  • (2020)High utility itemset mining using dolphin echolocation optimizationJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02571-1Online publication date: 3-Oct-2020

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cover image ACM Other conferences
MISNC, SI, DS 2016: Proceedings of the The 3rd Multidisciplinary International Social Networks Conference on SocialInformatics 2016, Data Science 2016
August 2016
371 pages
ISBN:9781450341295
DOI:10.1145/2955129
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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Publication History

Published: 15 August 2016

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Author Tags

  1. ant colony system
  2. ant system
  3. evolutionary computation
  4. high-utility itemsets

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MISNC, SI, DS 2016

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MISNC, SI, DS 2016 Paper Acceptance Rate 57 of 97 submissions, 59%;
Overall Acceptance Rate 57 of 97 submissions, 59%

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Cited By

View all
  • (2022)An overview of high utility itemsets mining methods based on intelligent optimization algorithmsKnowledge and Information Systems10.1007/s10115-022-01741-164:11(2945-2984)Online publication date: 3-Sep-2022
  • (2022)A survey on soft computing-based high-utility itemsets miningSoft Computing10.1007/s00500-021-06613-426:13(6347-6392)Online publication date: 25-Jan-2022
  • (2020)High utility itemset mining using dolphin echolocation optimizationJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02571-1Online publication date: 3-Oct-2020

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