Meta-learning based evolutionary clustering algorithm

2Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.
Get full text

Abstract

In this work, we address the hard clustering problem. We present a new clustering algorithm based on evolutionary computation searching a best partition with respect to a given quality measure. We present 32 partition transformation that are used as mutation operators. The algorithm is a (1 + 1 ) evolutionary strategy that selects a random mutation on each step from a subset of preselected mutation operators. Such selection is performed with a classifier trained to predict usefulness of each mutation for a given dataset. Comparison with state-of-the-art approach for automated clustering algorithm and hyperparameter selection shows the superiority of the proposed algorithm.

Cite

CITATION STYLE

APA

Tomp, D., Muravyov, S., Filchenkov, A., & Parfenov, V. (2019). Meta-learning based evolutionary clustering algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11871 LNCS, pp. 502–513). Springer. https://doi.org/10.1007/978-3-030-33607-3_54

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free