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- W4220924891 abstract "In the past few years, e-commerce business has placed a greater emphasis on delivering the better customer service. Building stronger customer relationships aids businesses in generating profits as well as customer happiness and retention. Customer segmentation is a useful tool for identifying unmet customer demand. This work solves the customer segmentation problem by improving the performance of K-means clustering algorithm by optimizing Sum of Squared Error using Elbow method. To aid K-means Clustering in calculating the optimal number of clusters, the Sum of Squared Error is used." @default.
- W4220924891 created "2022-04-03" @default.
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- W4220924891 date "2022-01-25" @default.
- W4220924891 modified "2023-10-16" @default.
- W4220924891 title "Automatic and Adaptive Segmentation of Customer in R framework using K-means Clustering Technique" @default.
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- W4220924891 doi "https://doi.org/10.1109/iccci54379.2022.9741067" @default.
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