Searching Cheapest Product On Three Different E-Commerce Using K-Means Algorithm

被引:0
|
作者
Prasetyo, Vincentius Riandaru [1 ]
机构
[1] Univ Surabaya, Dept Informat Engn, Surabaya, Indonesia
关键词
e-commerce; clustering; k-means; cosine similarity; precision; recall;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The development of e-commerce is starting to change people's lifestyles, not least the people of Indonesia. The existence of e-commerce is very helpful user in buying and selling products. There are many e-commerce that can be found today. Some of the famous e-commerce in Indonesia are Bukalapak, Lazada, and Blibli. A large number of existing e-commerce makes users, especially buyers, have difficulty when looking for products at the cheapest price. This happens because each e-commerce offers different prices for the same product. This research aims to make the cheapest product search system in Bukalapak, Lazada, and Blibli using K-Means algorithm. The results of experiments showed that K-Means algorithm can be used to classify product data from Bukalapak, Lazada, and Blibli well. The results of the clustering process can also help for searching the cheapest products from the three e-commerce becomes faster. However, the number of clusters used will affect the effectiveness of the search process on the system.
引用
收藏
页码:239 / 244
页数:6
相关论文
共 50 条
  • [21] A K-means plus plus Based User Classification Method for Social E-commerce
    Cui, Haoliang
    Niu, Shaozhang
    Li, Keyue
    Shi, Chengjie
    Shao, Shuai
    Gao, Zhenguang
    INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2021, 28 (01): : 277 - 291
  • [22] A COMBINATION K-MEANS CLUSTERING AND 2-OPT ALGORITHM FOR SOLVING THE TWO ECHELON E-COMMERCE LOGISTIC DISTRIBUTION
    Zuhanda, Muhammad Khahfi
    Suwilo, Saib
    Sitompul, Opim Salim
    Mardiningsih
    LOGFORUM, 2022, 18 (02) : 213 - 225
  • [23] Improved K-means Based on Flink Platform and Its Application in E-commerce Big Data
    Li, Chengfei
    Tian, Guo
    Cai, KunPeng
    2017 CHINESE AUTOMATION CONGRESS (CAC), 2017, : 7261 - 7264
  • [24] E-commerce recommender system based on improved K-means commodity information management model
    Zhang, Wei
    Wu, Zonghua
    HELIYON, 2024, 10 (09)
  • [25] A Local Algorithm for Product Return Prediction in E-Commerce
    Zhu, Yada
    Li, Jianbo
    He, Jingrui
    Quanz, Brian L.
    Deshpande, Ajay A.
    PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2018, : 3718 - 3724
  • [26] B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM
    Xiahou, Xiancheng
    Harada, Yoshio
    JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH, 2022, 17 (02): : 458 - 475
  • [27] Intelligent evaluation model of e-commerce transaction volume based on the combination of k-means and SOM algorithms
    Niu J.
    International Journal of Information and Communication Technology, 2021, 18 (02) : 189 - 206
  • [28] Soil data clustering by using K-means and fuzzy K-means algorithm
    Hot, Elma
    Popovic-Bugarin, Vesna
    2015 23RD TELECOMMUNICATIONS FORUM TELFOR (TELFOR), 2015, : 890 - 893
  • [29] RETRACTION: A study on e-commerce customer segmentation management based on improved K-means algorithm (Retraction of Vol 18, Pg 497, 2020)
    Deng, Yulin
    Gao, Qianying
    INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT, 2023, 21 (SUPPL 1) : 23 - 23
  • [30] Pattern Discovery Using K-Means Algorithm
    Ahmed, Almahdi Mohammed
    Norwawi, Norita Md
    Ishak, Wan Hussain Wan
    Alkilany, Ahmed
    2014 WORLD CONGRESS ON COMPUTER APPLICATIONS AND INFORMATION SYSTEMS (WCCAIS), 2014,