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|Abstract=Text clustering typically involves clustering in a high dimensional space, which appears difficult with regard to virtually all practical settings. In addition, given a particular clustering result it is typically very hard to come up with a good explanation of why the text clusters have been constructed the way they are. In this paper, we propose a new approach for applying background knowledge during preprocessing in order to improve clustering results and allow for selection between results. We preprocess our input data applying an ontology-based heuristics for feature selection and feature aggregation. Thus, we construct a number of alternative text representations. Based on these representations, we compute multiple clustering results using K-Means. The results may be distinguished and explained by the corresponding selection of concepts in the ontology. Our results compare favourably with a sophisticated baseline preprocessing strategy. | |Abstract=Text clustering typically involves clustering in a high dimensional space, which appears difficult with regard to virtually all practical settings. In addition, given a particular clustering result it is typically very hard to come up with a good explanation of why the text clusters have been constructed the way they are. In this paper, we propose a new approach for applying background knowledge during preprocessing in order to improve clustering results and allow for selection between results. We preprocess our input data applying an ontology-based heuristics for feature selection and feature aggregation. Thus, we construct a number of alternative text representations. Based on these representations, we compute multiple clustering results using K-Means. The results may be distinguished and explained by the corresponding selection of concepts in the ontology. Our results compare favourably with a sophisticated baseline preprocessing strategy. | ||
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|Download=2002_19_Hotho_Text_Clustering_1.pdf | |Download=2002_19_Hotho_Text_Clustering_1.pdf | ||
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Aktuelle Version vom 2. November 2009, 12:52 Uhr
Text Clustering Based on Good Aggregations
Text Clustering Based on Good Aggregations
Veröffentlicht: 2002
Journal: Künstliche Intelligenz (KI)
Nummer: 4
Seiten: 48-54
Volume: 16
Referierte Veröffentlichung
Kurzfassung
Text clustering typically involves clustering in a high dimensional space, which appears difficult with regard to virtually all practical settings. In addition, given a particular clustering result it is typically very hard to come up with a good explanation of why the text clusters have been constructed the way they are. In this paper, we propose a new approach for applying background knowledge during preprocessing in order to improve clustering results and allow for selection between results. We preprocess our input data applying an ontology-based heuristics for feature selection and feature aggregation. Thus, we construct a number of alternative text representations. Based on these representations, we compute multiple clustering results using K-Means. The results may be distinguished and explained by the corresponding selection of concepts in the ontology. Our results compare favourably with a sophisticated baseline preprocessing strategy.
Download: Media:2002_19_Hotho_Text_Clustering_1.pdf
Betriebliche Informationssysteme,Komplexitätsmanagement