The Cluster Analysis of Online Shop Product Reviews Using K-Means Clustering

Autor:	Eviyanti Novita Purba, Rena Nainggolan
Rok vydání:	2020
Předmět:	Service (business) Service quality Product reviews Computer science media_common.quotation_subject k-means clustering Product (category theory) Decision-making Payment Cluster analysis Data science media_common
Zdroj:	Data Science: Journal of Computing and Applied Informatics. 4:111-121
ISSN:	2580-829X 2580-6769
DOI:	10.32734/jocai.v4.i2-2855
Popis:	Technological developments have made changes in people's lifestyles, namely changes in the behavior of people who had shopped directly or offline to online. Many benefits are obtained from shopping online, namely the many conveniences offered by shopping online, besides that there are also many disadvantages of shopping online, namely the many risks in using e-commerce facilities, namely the problem of product or service quality, safety in payments, fraud. This research aims to mine review data on one of the e-commerce sites which ultimately produces clusters using the K-Means Clustering algorithm that can help potential customers to make a decision before deciding to buy a product or service
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7fbf59b040633e61d771c2ee0dd00676 https://doi.org/10.32734/jocai.v4.i2-2855 Zobrazit plný text záznamu