Authors:

Akshaykumar Padhiyar

Abstract:

Large amounts of both structured and unstructured data, sometimes known as "big data," these days offer prospects for businesses, particularly those engaged in electronic commerce (e-commerce). The customer's internal operations, vendors, marketplaces, and business environment are the sources of the data. The three common algorithms of association, grouping, and prediction are included in the data mining (DM) process for e-commerce presented in this work. Additionally, it outlines some of the advantages of data mining (DM) for online retailers, including the ability to use the three algorithms to do market segmentation, basket analysis, item planning, sales forecasting, and customer relationship management.

This paper's primary goal is to examine how data mining is used in e-commerce, with a particular emphasis on structured and unstructured data that is gathered from a variety of sources and cloud computing services. This will help to demonstrate the value of data mining. Additionally, this study assesses some of the difficulties associated with data mining, such as spider detection, data transformations, and providing business users with an understandable data model. Additional difficulties in accommodating the slowly changing dimensions of data, as well as facilitating data transformation and model building for business users, are also assessed.

This article also offers e-commerce organizations that own vast amounts of data a clear roadmap on how to leverage that data for company improvement, making them more competitive with their rivals.

Keywords:

Data Mining, Structure Data, Unstructured Data, Big Data, E-Commerce, Cloud Computing etc.

Vol & Issue:

VOL.16, ISSUE No.1, March 2024