Clustering of Non-Associated Item Sets for Analyzing Show Room Sales Dataset

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Published: Oct 7, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i11s.8175
Keywords:
Item Sets, Clustering, Show Room Sales, machine learning, CNAIS algorithm

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Vinayababu M
Research Scholar, Department of Computer Science, Sri Venkateswara University, Tirupati, India
M Sreedevi
Department of Computer Science, Sri Venkateswara University, Tirupati, India

Abstract

Market basket analysis (MBA) is a well-liked method for identifying relationships between products that people purchase in a database. It is predicated on association rule mining (ARM), a data mining technique that pulls valuable data from huge databases. Due to consumers using internet applications for online shopping and insurance, an ever-increasing amount of data is generated online. It produces large amounts and, if mined effectively, will greatly benefit society as a whole as well as individuals. So, numerous data science and machine learning-related techniques have been created to gradually unlock the potential. The Clustering of Non-Associated Item Sets (CNAIS) of the Sales dataset used in the Showroom for choosing customers for benefits and web application design is discussed in this study. The CNAIS algorithm implementation process and dataset for this study are discussed.

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How to Cite
M, V. ., & Sreedevi, M. . (2023). Clustering of Non-Associated Item Sets for Analyzing Show Room Sales Dataset. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11s), 461–468. https://doi.org/10.17762/ijritcc.v11i11s.8175
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Vol. 11 No. 11s (2023)
Section
Articles

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