Abstract
In this study, we proposed a clustering method based on Mini Batch K-means with principal component analysis for customer segmentation in e-commerce big data. Taking the Kaggle dataset: Customer Personality Analysis, for example, the Mini Batch K-means method is obviously superior to other algorithms. Compared with K-means, Agglomerative, Birch, and Spectral methods, the Mini Batch K-means method has the smallest sum of squares of errors SSE. Through the Mini Batch K-Means with PCA clustering method, we divide e-commerce customers into five clusters. After exploring the characteristics of these five clusters, the corresponding precision marketing strategies were proposed.
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