Knowledge Discovery for Scalable Data Mining

Chhabra, Indu and Suri, Gunmala (2019) Knowledge Discovery for Scalable Data Mining. EAI Endorsed Transactions on Scalable Information Systems, 6 (21): e3. ISSN 2032-9407

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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.

Item Type: Article
Uncontrolled Keywords: Association Mining, Knowledge Discovery, Influential Factors, Post Purchase Behavior and Retail Industry
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 08 Oct 2020 13:55
Last Modified: 08 Oct 2020 13:55
URI: https://eprints.eudl.eu/id/eprint/706

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