Towards Performance Overview of Mini Batch K-Means and K-Means: Case of Four-Wheeler Market Segmentation

Abstract

Market segmentation allows companies to target right product and advertising to the right customers, thereby enhancing the performance of their marketing campaigns. Market segmentation process is aided by clustering algorithms. Mini Batch K-means (MBK) is an enhanced K-means clustering algorithm which has proved to be efficient in terms of computation speed and space utilization for large datasets, tested in many applications. However, extant literature has shown that it has quality issues with an increase in the number of clusters formed in large datasets. Therefore, the purpose of the present work is to assess the performance of Mini Batch K-means algorithm which is then compared to the standard K-means algorithm using the performance parameters such as quality of clusters and computational speed for small four-wheeler market segmentation dataset. The results revealed that Mini batch K-means cluster quality was affected by the number of clusters whereas K-means was not much affected. However, for computational time assessment, Mini batch K-means was much slower than K-means for small datasets.