S-FINCH: An Optimized Streaming Adaptation to FINCH Clustering

Real-world datasets are growing ever larger and more dynamic, motivating the need for efficient online data exploration algorithms. In this work, we present S-FINCH, a streaming domain optimization of the recent FINCH clustering method. The original FINCH approach demonstrated state-of-the-art offline performance while avoiding sensitive hyperparameters but was not designed for the online data streaming domain. The proposed S-FINCH method is an exact, incremental update to the FINCH approach which generates high-quality clusters while avoiding the potential sensitivities of other online clustering methods. We also provide alternative cluster tree representatives for faster empirical cluster times. Experiments are shown comparing the original FINCH approach to the proposed S-FINCH method in a streaming domain with multiple benchmark synthetic and real datasets. The S-FINCH method leverages the ability to make local changes for efficient, real time updates and can be applied to multiple data streaming scenarios.