SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting. (arXiv:2206.12190v1 [cs.LG])

Sequence clustering in a streaming environment is challenging because it is
computationally expensive, and the sequences may evolve over time. K-medoids or
Partitioning Around Medoids (PAM) is commonly used to cluster sequences since
it supports alignment-based distances, and the k-centers being actual data
items helps with cluster interpretability. However, offline k-medoids has no
support for concept drift, while also being prohibitively expensive for
clustering data streams. We therefore propose SECLEDS, a streaming variant of
the k-medoids algorithm with constant memory …

arxiv clustering data data streams lg voting