Open AccessK-means Clustering Algorithm Based on E-Commerce Big Data
Author(s) -
Indivar Shaik,
Swapna Suhasini Nittela,
Tryambak Hiwarkar,
Srinivas Nalla
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.k2121.0981119
Subject(s) - cluster analysis , computer science , data mining , partition (number theory) , cure data clustering algorithm , big data , correlation clustering , data stream clustering , task (project management) , canopy clustering algorithm , identification (biology) , consensus clustering , artificial intelligence , mathematics , engineering , botany , systems engineering , combinatorics , biology
As the technology improving, huge volumes of different types of data is being generated rapidly. Mining such data is a challenging task. One of the important tasks of mining is to group similar objects or similar data into cluster which is very much useful for analysis and prediction. K-means clustering method is a popular partition based approach for clustering data as it leads to good quality of results. This paper focuses on K-means clustering algorithm by analyzing the E-commerce big data. In this research, geographical location and unique identification number of the customer are considered as constraints for clustering