Hierarchical Clustering Techniques From Mixed Data Sequences

Abstract

Through the efficient organization of massive volumes of data into a small number of relevant clusters, high-quality document clustering algorithms play a crucial role in facilitating straightforward navigation and browsing procedures. Data stream hierarchical clustering approaches are covered, as with comparisons of algorithmic performance. Furthermore, this study explains and compares many data clustering algorithms. The standard datasets are used as input, and the appropriate hierarchical clustering technique is then used to them. The output should be clustered data that is well-versed and appropriately ordered. Microbial community analysis relies heavily on taxonomy-free methods of investigation. Many subsequent studies rely on identifying operational taxonomic units, and hierarchical clustering is one of the most popular methods for doing so. Because of their quadratic space and computing difficulties, most known methods are limited in their applicability to situations of moderate size or less. To solve the space and computational bottlenecks of existing solutions, we offer a novel online learning-based technique.