Knowledge Acquisition Via Incremental Conceptual Clustering
D. Fisher. Machine Learning, 2 (2):
139--172(September 1987)
Zusammenfassung
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.
%0 Journal Article
%1 fischer87
%A Fisher, Douglas H.
%D 1987
%J Machine Learning
%K clustering climbing incremental learning hill inference concept formation conceptual
%N 2
%P 139--172
%T Knowledge Acquisition Via Incremental Conceptual Clustering
%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.
@article{fischer87,
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.},
added-at = {2006-03-23T12:22:43.000+0100},
author = {Fisher, Douglas H.},
biburl = {https://www.bibsonomy.org/bibtex/20edbe48f91025efea4af0a1a62433e42/hotho},
interhash = {36208ac57cc67951de85bd99b8fb8647},
intrahash = {0edbe48f91025efea4af0a1a62433e42},
journal = {Machine Learning},
keywords = {clustering climbing incremental learning hill inference concept formation conceptual},
month = {September},
number = 2,
pages = {139--172},
timestamp = {2006-03-23T12:22:43.000+0100},
title = {Knowledge Acquisition Via Incremental Conceptual Clustering},
volume = 2,
year = 1987
}