Market Segmentation Analysis and Visualization using K-Mode Clustering Algorithm for E-Commerce Business
Author(s) -
Deepali Kamthania,
Ashish Pawa,
Sirijit Madhavan
Publication year - 2018
Publication title -
journal of computing and information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.169
H-Index - 27
eISSN - 1846-3908
pISSN - 1330-1136
DOI - 10.20532/cit.2018.1003863
Subject(s) - computer science , business intelligence , cluster analysis , market segmentation , segmentation , visualization , mode (computer interface) , k means clustering , process (computing) , component (thermodynamics) , principal component analysis , data mining , artificial intelligence , machine learning , marketing , human–computer interaction , business , operating system , physics , thermodynamics
Now, all business organizations are adopting data driven strategies to generate more profits out of their business. Growing startups are investing a lot of funds in data economy to maximize profits of the business group by developing intelligent tools backed by machine learning and artificial intelligence. The nature of business intelligence (BI) tool depends on factors like business goals, size, model, technology, etc. In this paper, the architecture of BI tool and decision process has been discussed with a focus on market segmentation, based on user behavior geographical distributions. Principal Component Analysis (PCA) followed by k-mode clustering algorithm has been used for segmentation. The proposed toolkit also incorporates interactive visualizations and maps.
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