Knowledge Acquisition Via Incremental Conceptual Clustering
D. Fisher.
Machine Learning 2 (2): 139--172 (September 1987)

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 -

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@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  -},
  added-at = {2010-05-04T08:55:46.000+0200},
  author = {Fisher, Douglas H.},
  biburl = {https://puma.uni-kassel.de/bibtex/20edbe48f91025efea4af0a1a62433e42/folke},
  interhash = {36208ac57cc67951de85bd99b8fb8647},
  intrahash = {0edbe48f91025efea4af0a1a62433e42},
  journal = {Machine Learning},
  keywords = {COMMUNE classit clustering community coweb detection},
  month = {#sep#},
  number = 2,
  pages = {139--172},
  timestamp = {2010-05-04T08:55:47.000+0200},
  title = {Knowledge Acquisition Via Incremental Conceptual Clustering},
  url = {http://dx.doi.org/10.1023/A:1022852608280},
  volume = 2,
  year = 1987
}

%0 Journal Article
%1 h1987knowledge
%A Fisher, Douglas H.
%D 1987
%J Machine Learning
%K COMMUNE classit clustering community coweb detection
%N 2
%P 139--172
%T Knowledge Acquisition Via Incremental Conceptual Clustering
%U http://dx.doi.org/10.1023/A:1022852608280
%V 2
%X 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  -

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Knowledge Acquisition Via Incremental Conceptual Clustering
D. Fisher.
Machine Learning 2 (2): 139--172 (September 1987)

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PUMA

Knowledge Acquisition Via Incremental Conceptual ClusteringD. Fisher. Machine Learning 2 (2): 139--172 (September 1987)

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Cite this publication

Knowledge Acquisition Via Incremental Conceptual Clustering
D. Fisher.
Machine Learning 2 (2): 139--172 (September 1987)