A Maximal Heterogeneity Based Clustering Approach for Obtaining Samples

TL;DR

This paper introduces the Wobbly Center Algorithm, a novel clustering-based sampling method that maximizes heterogeneity within samples, providing more consistent and representative samples for medical and social science applications.

Contribution

The paper presents a new non-statistical, no-replacement sampling technique that builds clusters by maximizing internal heterogeneity, outperforming existing sampling methods.

Findings

Wobbly Center Algorithm produces more consistent samples.

Statistical validation shows samples are representative.

Algorithm outperforms other sampling techniques on benchmark datasets.

Abstract

Medical and social sciences demand sampling techniques which are robust, reliable, replicable and have the least dissimilarity between the samples obtained. Majority of the applications of sampling use randomized sampling, albeit with stratification where applicable. The randomized technique is not consistent, and may provide different samples each time, and the different samples themselves may not be similar to each other. In this paper, we introduce a novel non-statistical no-replacement sampling technique called Wobbly Center Algorithm, which relies on building clusters iteratively based on maximizing the heterogeneity inside each cluster. The algorithm works on the principle of stepwise building of clusters by finding the points with the maximal distance from the cluster center. The obtained results are validated statistically using Analysis of Variance tests by comparing the samples obtained to check if they are representative of each other. The obtained results generated from running the Wobbly Center algorithm on benchmark datasets when compared against other sampling algorithms indicate the superiority of the Wobbly Center Algorithm.