Unsupervised Clustering of Multivariate Sports Activity Data Using K-Means: A Study on the Sport Data Multivariate Time Series Dataset

Abstract

This work investigates the combination of unsupervised machine learning with blockchain- influenced data integrity aspects on multivariate time series (MTS) sports activity data. Using the SportData MTS dataset with complex physiological and movement parameters such as heart rate, speed, and altitude, we used K-Means clustering to uncover hidden patterns in the data and incorporated blockchain-influenced hash chains for traceability and integrity of data. Each of the datasets was standardized to ensure equal scaling, and three clusters were identified using silhouette score and elbow method evaluation. The result confirms K-Means to effectively cluster the data into tightly separated groups, with principal component analysis (PCA) plots confirming that there is substantial separation. Silhouette score analysis also confirmed the compactness and separability of groups. In addition, blockchain-inspired hashing was applied to each record to simulate tamper- evidence, providing a firm grounding for secure machine learning pipelines. The end-to-end solution not only reveals the inherent structure in sports activity data but also hints at maintaining data integrity to provide sound and transparent machine learning results, paving the way for future work in secure sports analytics, activity recognition, and anomaly detection.