Comparison of Automatic Clustering and Manual Categorization of Documents
15th International Workshops on Conceptual Structures
The fundamental goal of this research is to learn whether unsupervised learning can be used to cluster documents in the collection in a similar way that manual categories are. We report on our experiments with K-mean clustering algorithm to provide a partial answer to the above mentioned goal.
Cluster analysis – Computer programs; Document clustering; Machine learning
Computer Engineering | Electrical and Computer Engineering | Theory and Algorithms
Use Find in Your Library, contact the author, or interlibrary loan to garner a copy of the item. Publisher policy does not allow archiving the final published version. If a post-print (author's peer-reviewed manuscript) is allowed and available, or publisher policy changes, the item will be deposited.
Comparison of Automatic Clustering and Manual Categorization of Documents.
Presentation at 15th International Workshops on Conceptual Structures,
Available at: http://digitalscholarship.unlv.edu/ece_presentations/26