{"id":{"repo_id":"arizona-thes","oai_identifier":"oai:repository.arizona.edu:10150/187257"},"canonical_url":"https://search.dev.ndltd.org/etd/arizona-thes/oai:repository.arizona.edu:10150/187257","repository":{"repo_id":"arizona-thes","name":"University of Arizona","base_url":"https://repository.arizona.edu/oai/request"},"display":{"title":"A graphical, self-organizing approach to classifying electronic meeting output.","abstract":"This dissertation describes research in the application and evaluation of a Kohonen Self-Organizing Map (SOM) to the problem of classification of Electronic Brainstorming output. Electronic Brainstorming is one of the most productive tools in the Electronic Meeting System called GroupSystems. A major step in group problem solving involves the classification of Electronic Brainstorming output into a manageable list of concepts, topics, or issues that can be further evaluated by the group. This step is problematic due to the information overload and cognitive load of the large quantity of data. This research builds upon previous work in automating the classification process using a Hopfield Neural Network. Evaluation of the Kohonen output in comparison with the Hopfield and human expert output over the same set of data found that the Kohonen SOM performed as well as a human expert in the recollection of associated term pairs and outperformed the Hopfield Neural Network algorithm. Using information retrieval measures, recall of concepts using the Kohonen algorithm was equivalent to the human expert. However, precision was poor. The graphical representation of textual data produced by the Kohonen SOM suggests many opportunities for improving information management of textual electronic information. Increasing uses of electronic mail, computer-based bulletin board systems, and world-wide web textual data suggest an overwhelming amount of textual information to manage. This research suggests that the Kohonen SOM may be used to automatically create \"a picture that can represent a thousand (or more) words.\"","abstract_html":"This dissertation describes research in the application and evaluation of a Kohonen Self-Organizing Map (SOM) to the problem of classification of Electronic Brainstorming output. Electronic Brainstorming is one of the most productive tools in the Electronic Meeting System called GroupSystems. A major step in group problem solving involves the classification of Electronic Brainstorming output into a manageable list of concepts, topics, or issues that can be further evaluated by the group. This step is problematic due to the information overload and cognitive load of the large quantity of data. This research builds upon previous work in automating the classification process using a Hopfield Neural Network. Evaluation of the Kohonen output in comparison with the Hopfield and human expert output over the same set of data found that the Kohonen SOM performed as well as a human expert in the recollection of associated term pairs and outperformed the Hopfield Neural Network algorithm. Using information retrieval measures, recall of concepts using the Kohonen algorithm was equivalent to the human expert. However, precision was poor. The graphical representation of textual data produced by the Kohonen SOM suggests many opportunities for improving information management of textual electronic information. Increasing uses of electronic mail, computer-based bulletin board systems, and world-wide web textual data suggest an overwhelming amount of textual information to manage. This research suggests that the Kohonen SOM may be used to automatically create "a picture that can represent a thousand (or more) words."","abstract_has_math":false,"creators":["Orwig, Richard Eldon."],"institution":"The University of Arizona.","degree_name":"Ph.D.","degree_level":"doctoral","degree_discipline":"Business Administration","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":["Nunamaker, Jay F. Jr.","Vogel, Douglas"],"year":1995,"date_issued":"1995","date_published":"1995","updated_at":"2026-07-24T00:56:22Z","subjects":["Software architecture.","Computer software -- Human factors.","Graphical user interfaces (Computer systems)"],"languages":["en"],"rights":["Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. 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A major step in group problem solving involves the classification of Electronic Brainstorming output into a manageable list of concepts, topics, or issues that can be further evaluated by the group. This step is problematic due to the information overload and cognitive load of the large quantity of data. This research builds upon previous work in automating the classification process using a Hopfield Neural Network. Evaluation of the Kohonen output in comparison with the Hopfield and human expert output over the same set of data found that the Kohonen SOM performed as well as a human expert in the recollection of associated term pairs and outperformed the Hopfield Neural Network algorithm. Using information retrieval measures, recall of concepts using the Kohonen algorithm was equivalent to the human expert. However, precision was poor. The graphical representation of textual data produced by the Kohonen SOM suggests many opportunities for improving information management of textual electronic information. Increasing uses of electronic mail, computer-based bulletin board systems, and world-wide web textual data suggest an overwhelming amount of textual information to manage. This research suggests that the Kohonen SOM may be used to automatically create \"a picture that can represent a thousand (or more) words.\""]},{"key":"dc:title","label":"Title","values":["A graphical, self-organizing approach to classifying electronic meeting output."]}]}],"canonical_facts":{"dc:contributor.committeemember":["Nunamaker, Jay F. Jr.","Vogel, Douglas"],"dc:creator":["Orwig, Richard Eldon."],"dc:date.accessioned":["2011-10-31T18:34:27Z"],"dc:date.available":["2011-10-31T18:34:27Z"],"dc:date.issued":["1995"],"dc:description.abstract":["This dissertation describes research in the application and evaluation of a Kohonen Self-Organizing Map (SOM) to the problem of classification of Electronic Brainstorming output. Electronic Brainstorming is one of the most productive tools in the Electronic Meeting System called GroupSystems. A major step in group problem solving involves the classification of Electronic Brainstorming output into a manageable list of concepts, topics, or issues that can be further evaluated by the group. This step is problematic due to the information overload and cognitive load of the large quantity of data. This research builds upon previous work in automating the classification process using a Hopfield Neural Network. 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