Optimizing K-Means Clustering Parameters for Mapping Smart Contract Transaction Characteristics: A Comparative Analysis of Evaluation Metrics in the IOTA Ecosystem

Abstract

Smart contracts are already a major development in digital transaction automation thanks to blockchain technology, but their operational efficiency is still greatly impacted by resource consumption, transaction success rates, and gas cost dynamics. This study aims to optimize the K-Means Clustering algorithm's parameters in order to map the characteristics of smart contract transactions in the IOTA ecosystem and provide thorough insights into the efficiency of gas allocation. Using a massive dataset of 566,303 empirical transactions from the IOTA Tangle, three key metrics the Silhouette Coefficient, Davies-Bouldin Index, and Calinski-Harabasz Index were compared to verify the quality of the clustering. With a Silhouette Coefficient value of 0.9851, Davies-Bouldin Index of 0.4622, and Calinski-Harabasz Index of 741,423.92, quantitative evaluation results demonstrate that the 3- cluster structure performs better than two clusters. These results validate the 3-cluster model's ability to more accurately divide transactions into categories that are efficient, complex, and gas-inefficient. The results of this mapping can serve as the foundation for creating an automated recommendation system for optimizing transaction costs in decentralized networks. This study shows that the Gas Limit and Gas Consumed indicators are crucial predictors of transaction efficiency