@article{h1987knowledge,
  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.
ER  -},
  author = {Fisher, Douglas H.},
  interhash = {36208ac57cc67951de85bd99b8fb8647},
  intrahash = {0edbe48f91025efea4af0a1a62433e42},
  journal = {Machine Learning},
  month = {#sep#},
  number = 2,
  pages = {139--172},
  title = {Knowledge Acquisition Via Incremental Conceptual Clustering},
  url = {http://dx.doi.org/10.1023/A:1022852608280},
  volume = 2,
  year = 1987
}