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    • Thesis & Report, BSc (Computer Science and Engineering)
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    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Market basket analysis for improving the effectiveness of marketing and sales using Apriori, FP Growth and Eclat Algorithm

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    13301128,13301038,13301125_CSE.pdf (1.158Mb)
    Date
    8/21/2017
    Publisher
    BRAC Univeristy
    Author
    Khan, Mohammod Akib
    Solaiman, Kazi Mohammad
    Pritom, Touhid Hossain
    Metadata
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    URI
    http://hdl.handle.net/10361/8693
    Abstract
    Data mining approach with the help of best frequent pattern extracting algorithm can have a big impact in the field of marketing and sales. Frequent pattern mining is a widely researched field in data mining because of its importance in many real life applications. In this thesis, we used the three most popular algorithms in frequent pattern mining for market basket analysis – FP Growth, Apriori, and Eclat. The design and implementation of these three pattern mining algorithms were discussed in detail. All the three algorithms gave consistent output. We did performance comparison and analysis of these algorithms using three different datasets. Recommendations are provided to suggest the best algorithm to use in different contexts.
    Keywords
    Eclat algorithm; Apriori; Market analysis; Data mining; Frequent pattern
     
    Description
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (pages 59-61).
     
    This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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