This research aims to present a technical analysis of the Traversal Optimisation Algorithm (TOA) for clustering and K-means clustering algorithm. The goal is to rigorously test this algorithm against different data specifications beyond what has previously been used with K-means without artificially and subjectively setting the initial number of clusters. The experimental evaluation involve the use of diverse cluster optimisation techniques for K-means while applying a wider range of internal validation methods such as Davies-Bouldin Index, Dunn Index and Silhouette Method, for appraising cluster quality of the Traversal Optimisation Algorithm, while at the same time not compromising the configuration of the default algorithm. The findings in this work shows that the optimisation algorithm’s clustering quality as calculated by multiple internal validity indices can be very poor when operating on datasets with varying characteristics. This is owing to the algorithm’s lack of any add-on mechanism for computing the optimal number of clusters that a dataset needs apriori. The results reveal that in a data processing contexts where the number of clusters are specified, the TOA yields a favourable cost-benefit in terms of run-time complexity and clustering quality.