| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Hotho, A., Staab, S. & Stumme, G. | Text Clustering Based on Background Knowledge | 2003 | Vol. 425 |
techreport | URL |
| Abstract: Text document clustering plays an important role in providing intuitive
vigation and browsing mechanisms by organizing large amounts of information to a small number of meaningful clusters. Standard partitional or agglomerative ustering methods efficiently compute results to this end. wever, the bag of words representation used for these clustering methods is often satisfactory as it ignores relationships between important terms that do not -occur literally. Also, it is mostly left to the user to find out why a particular partitioning s been achieved, because it is only specified extensionally. In order to al with the two problems, we integrate background knowledge into the process of ustering text documents. rst, we preprocess the texts, enriching their representations by background knowledge ovided in a core ontology — in our application Wordnet. Then, we cluster e documents by a partitional algorithm. Our experimental evaluation on Reuters wsfeeds compares clustering results with pre-categorizations of news. In the experiments, provements of results by background knowledge compared to the baseline n be shown for many interesting tasks. cond, the clustering partitions the large number of documents to a relatively small mber of clusters, which may then be analyzed by conceptual clustering. In our approach, applied Formal Concept Analysis. Conceptual clustering techniques are own to be too slow for directly clustering several hundreds of documents, but they ve an intensional account of cluster results. They allow for a concise description commonalities and distinctions of different clusters. With background knowledge ey even find abstractions like “food” (vs. specializations like “beef” or “corn”). us, in our approach, partitional clustering reduces first the size of the problem ch that it becomes tractable for conceptual clustering, which then facilitates the derstanding of the results. |
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BibTeX:
@techreport{hotho03textclustering,
author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd},
title = {Text Clustering Based on Background Knowledge},
year = {2003},
volume = {425},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003text.pdf}
}
|
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| Stumme, G. | Attribute Exploration with Background Implications and Exceptions [BibTeX] |
1996 | Data Analysis and Information Systems. Statistical and Conceptual approaches. Proc. GfKl'95. Studies in Classification, Data Analysis, and Knowledge Organization 7, pp. 457-469 | inproceedings | URL |
BibTeX:
@inproceedings{stumme96attribute,
author = {Stumme, Gerd},
title = {Attribute Exploration with Background Implications and Exceptions},
booktitle = {Data Analysis and Information Systems. Statistical and Conceptual approaches. Proc. GfKl'95. Studies in Classification, Data Analysis, and Knowledge Organization 7},
publisher = {Springer},
year = {1996},
pages = {457-469},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1995/P1781-GfKl95.pdf}
}
|
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Created by JabRef on 25/04/2024.