Knowledge Acquisition via Incremental Conceptual Clustering

Fisher, Douglas H.

doi:10.1007/BF00114265

Knowledge Acquisition via Incremental Conceptual Clustering

Douglas H. Fisher

MLJ 1987 pp. 139-172

doi:10.1007/BF00114265 /mlj/1987/fisher1987mlj-knowledge/

Abstract

Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has not explicitly dealt with constraints imposed by real world environments. This article presents COBWEB, a conceptual clustering system that organizes data so as to maximize inference ability. Additionally, COBWEB is incremental and computationally economical, and thus can be flexibly applied in a variety of domains.

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Text

Fisher. "Knowledge Acquisition via Incremental Conceptual Clustering." Machine Learning, 1987. doi:10.1007/BF00114265

Markdown

[Fisher. "Knowledge Acquisition via Incremental Conceptual Clustering." Machine Learning, 1987.](https://mlanthology.org/mlj/1987/fisher1987mlj-knowledge/) doi:10.1007/BF00114265

BibTeX

@article{fisher1987mlj-knowledge,
  title     = {{Knowledge Acquisition via Incremental Conceptual Clustering}},
  author    = {Fisher, Douglas H.},
  journal   = {Machine Learning},
  year      = {1987},
  pages     = {139-172},
  doi       = {10.1007/BF00114265},
  volume    = {2},
  url       = {https://mlanthology.org/mlj/1987/fisher1987mlj-knowledge/}
}