Document clustering is a powerful tool for browsing throughout a document database. Similar documents are gathered into several clusters and a representative document of each cluster is shown to users. To make users infer the content of the database from several representatives, the documents must be separated into tight clusters, in which documents are connected with high similarities. At the same time, clustering must be fast for user interaction. We propose an O(n/sup 2/) time, O(n) space cluster extraction method. It is faster than the ordinal clustering methods, and its clusters compare favorably with those produced by Complete Link for tightness. When we deal with OCR-ed documents, term loss caused by recognition faults can change similarities between documents. We also examined the effect of recognition faults to the performance of document clustering.
Clustering OCR-ed texts for browsing document image database
Proceedings of 3rd International Conference on Document Analysis and Recognition ; 1 ; 171-174 vol.1
1995-01-01
427255 byte
Conference paper
Electronic Resource
English
Clustering OCR-ed Texts for Browsing Document Image Database
British Library Conference Proceedings | 1995
|A Document Browsing Tool: Using Lexical Classes to Convey Information
British Library Conference Proceedings | 2005
|Opaque Document Imaging: Building Images of Inaccessible Texts
British Library Conference Proceedings | 2005
|Information-theoretic ensemble clustering on web texts
British Library Online Contents | 2016
|