Examining Feasibility and Efficacy of Traditional Stream Clustering Algorithms on Complex Human Activity Recognition Data
Martin Woo, Farhana H. Zulkernine, Hanady M. Abdulsalam · 2023
Modern adoption of network-connected devices have led to an abundance of high speed data streams and a call for the development of analytical models capable of processing them. Much of the real world data is unlabeled which requires development and application of unsupervised learning algorithms for advanced machine perception. However, the datasets that are used to train and validate these algorithms are often synthetic or simulated for the study and too simple, which leads to an inflation of reported performance. Based on the recent literature, there is a lack in research on developing deep learning models for clustering highly complex Human Activity Recognition (HAR) data streams. We, therefore, explore the area and apply three modern high-performing stream clustering algorithms on a complex HAR data stream. We evaluate the ability of these models to adapt and extract the volatile knowledge that is contained within the HAR data streams. Our goal is to set a baseline of performance for clustering timestamped HAR data streams. The results show that despite the reported performance of these algorithms in the literature, they lack the analytical depth to effectively extract sufficient knowledge from the complex HAR streams to construct viable clusters. So, new thoughts are needed to develop models that can efficiently handle HAR data streams.