Incremental Clustering by Minimizing Representation Length
Abstract
We describe an incremental method of conceptual clustering for continuously valued data, which minimizes a cost function of a cluster configuration. This function is defined as the length of a reconstructive representation of data with the aid of clusters. The clustering program inserts each new instance to one of the clusters, updates the parameters of this cluster, and possibly divides it into smaller clusters. The program uses a novel prediction mechanism to decide when dividing a cluster might decrease the configuration cost.
Cite
Text
Segen. "Incremental Clustering by Minimizing Representation Length." International Conference on Machine Learning, 1989. doi:10.1016/B978-1-55860-036-2.50101-6Markdown
[Segen. "Incremental Clustering by Minimizing Representation Length." International Conference on Machine Learning, 1989.](https://mlanthology.org/icml/1989/segen1989icml-incremental/) doi:10.1016/B978-1-55860-036-2.50101-6BibTeX
@inproceedings{segen1989icml-incremental,
title = {{Incremental Clustering by Minimizing Representation Length}},
author = {Segen, Jakub},
booktitle = {International Conference on Machine Learning},
year = {1989},
pages = {400-403},
doi = {10.1016/B978-1-55860-036-2.50101-6},
url = {https://mlanthology.org/icml/1989/segen1989icml-incremental/}
}