(КПІ ім. Ігоря Сікорського, 2022) Lyubchyk, L. M.; Yamkovyi, K. S.
The paper is devoted to improving semi-supervised clustering methods
and comparing their accuracy and robustness. The proposed approach is based on
expanding a clustering algorithm for using an available set of labels by replacing the
distance function. Using the distance function considers not only spatial data but
also available labels. Moreover, the proposed distance function could be adopted for
working with ordinal variables as labels. An extended approach is also considered,
based on a combination of unsupervised k-medoids methods, modified for using
only labeled data during the medoids calculation step, supervised method of k nearest
neighbor, and unsupervised k-means. The learning algorithm uses information about
the nearest points and classes’ centers of mass. The results demonstrate that even a
small amount of labeled data allows us to use semi-supervised learning, and proposed
modifications improve accuracy and algorithm performance, which was found
during experiments.