PUMA publications for /author/Douglas%20H.%20Fisherhttps://puma.uni-kassel.de/author/Douglas%20H.%20FisherPUMA RSS feed for /author/Douglas%20H.%20Fisher2024-03-29T15:47:34+01:00Knowledge Acquisition Via Incremental Conceptual Clusteringhttps://puma.uni-kassel.de/bibtex/20edbe48f91025efea4af0a1a62433e42/folkefolke2010-05-04T08:55:46+02:00detection clustering coweb community COMMUNE classit <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Douglas H. Fisher" itemprop="url" href="/author/Douglas%20H.%20Fisher"><span itemprop="name">D. Fisher</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Machine Learning</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">2 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">139--172</span></em> </span>(<em><span>September 1987<meta content="September 1987" itemprop="datePublished"/></span></em>)Tue May 04 08:55:46 CEST 2010Machine Learning#sep#2139--172Knowledge Acquisition Via Incremental Conceptual Clustering21987detection clustering coweb community COMMUNE classit 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 -Knowledge Acquisition Via Incremental Conceptual Clusteringhttps://puma.uni-kassel.de/bibtex/20edbe48f91025efea4af0a1a62433e42/hothohotho2006-03-23T12:22:43+01:00inference conceptual concept climbing hill clustering formation incremental learning <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Douglas H. Fisher" itemprop="url" href="/author/Douglas%20H.%20Fisher"><span itemprop="name">D. Fisher</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Machine Learning</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">2 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">139--172</span></em> </span>(<em><span>September 1987<meta content="September 1987" itemprop="datePublished"/></span></em>)Thu Mar 23 12:22:43 CET 2006Machine LearningSeptember2139--172Knowledge Acquisition Via Incremental Conceptual Clustering21987inference conceptual concept climbing hill clustering formation incremental learning 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.