Research Article
Knowledge Discovery for Scalable Data Mining
@ARTICLE{10.4108/eai.19-3-2019.158527, author={Indu Chhabra and Gunmala Suri}, title={Knowledge Discovery for Scalable Data Mining}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={6}, number={21}, publisher={EAI}, journal_a={SIS}, year={2019}, month={5}, keywords={Association Mining, Knowledge Discovery, Influential Factors, Post Purchase Behavior and Retail Industry}, doi={10.4108/eai.19-3-2019.158527} }- Indu Chhabra
Gunmala Suri
Year: 2019
Knowledge Discovery for Scalable Data Mining
SIS
EAI
DOI: 10.4108/eai.19-3-2019.158527
Abstract
The scalable diverse data and increasing levels of complexity in engineering and management science have given a boost to Data mining technology. The purpose of the proposed research is to evaluate the rule-based technique to develop solutions for analyzing customer Post Purchase behavior through knowledge discovery paradigm of Association rule mining. Over the years, it has proved a good tool to predict because of the incorporation of actual mined patterns. The current work is focused on extracting knowledge about the customer purchasing psychology and behaviour for the most frequent item combinations. For the purchase implementation, association rule framework is assessed for its performance analysis. The inferences of this automated intelligent system are based on of real life data set of 120 item-set combinations of five computer peripherals. This knowledge will help in framing and executing the most appropriate market laws and rules for the overall business growth.
Copyright © 2019 Indu Chhabra et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
