TY - JOUR AU - Fisher, Douglas H. T1 - Knowledge Acquisition Via Incremental Conceptual Clustering JO - Machine Learning PY - 1987/10 VL - 2 IS - 2 SP - 139 EP - 172 UR - http://dx.doi.org/10.1023/A:1022852608280 M3 - KW - detection KW - clustering KW - coweb KW - community KW - COMMUNE KW - classit L1 - SN - N1 - N1 - AB - 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 - ER - TY - JOUR AU - Fisher, Douglas H. T1 - Knowledge Acquisition Via Incremental Conceptual Clustering JO - Machine Learning PY - 1987/10 VL - 2 IS - 2 SP - 139 EP - 172 UR - M3 - KW - inference KW - conceptual KW - concept KW - climbing KW - hill KW - clustering KW - formation KW - incremental KW - learning L1 - SN - N1 - N1 - AB - 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 -