Online clustering algorithm with a greedy agglomerative heuristic procedure and special distance measures
Guzel Sharipzhanovna Shkaberina, Leonid Verenev, Elena Tovbis, Lev Aleksandrovich Kazakovtsev · AIP conference proceedings · 2023
Automatic grouping (clustering) involves dividing a set of objects into subsets (groups) so that objects from one subset are more similar to each other than to objects from other subsets according to some criterion. There are industries where data come in the form of a stream, i.e. a sequence of potentially infinite, non-stationary data arriving continuously. In our study, we propose a greedy agglomerative heuristic algorithm with distance measure variations for online products clustering. Computational experiments demonstrate the comparative efficiency and accuracy of using the greedy agglomerative heuristic in online clustering of industrial products into homogeneous production batches.