Improving Inference Through Conceptual Clustering
Abstract
Conceptual clustering is an important way to sununarize data in an understandable manner. However, the recency of the conceptual clustering paradigm has allowed little exploration of conceptual clustering as a means of improving performance. This paper presents COBWEB, a conceptual clustering system that organizes data to maximize inference abilities. It does this by capturing attribute inter-correlations at classification tree nodes and generating inferences as a by-product of classification. Results from the domains of soybean and thyroid disease diagnosis support the success of this approach.
Cite
Text
Fisher. "Improving Inference Through Conceptual Clustering." AAAI Conference on Artificial Intelligence, 1987.Markdown
[Fisher. "Improving Inference Through Conceptual Clustering." AAAI Conference on Artificial Intelligence, 1987.](https://mlanthology.org/aaai/1987/fisher1987aaai-improving/)BibTeX
@inproceedings{fisher1987aaai-improving,
title = {{Improving Inference Through Conceptual Clustering}},
author = {Fisher, Douglas H.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1987},
pages = {461-465},
url = {https://mlanthology.org/aaai/1987/fisher1987aaai-improving/}
}