Evaluation of Hierarchical Clustering Algorithms for Document Datasets
2002-06-03
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Evaluation of Hierarchical Clustering Algorithms for Document Datasets
Alternative title
Authors
Published Date
2002-06-03
Publisher
Type
Report
Abstract
Fast and high-quality document clustering algorithms play animportant role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In particular, hierarchical clustering solutions provide a view of the data at different levels of granularity, making them ideal for people to visualize and interactively explore large document collections. The focus of this paper is to evaluate different hierarchical clustering algorithms and toward this goal we compared variouspartitional and agglomerative approaches. Our experimentalevaluation showed that partitional algorithms always lead tobetter clustering solutions than agglomerative algorithms, which suggests that partitional clustering algorithms are well-suited for clustering large document datasets due to not only their relatively low computational requirements, but also comparable or even better clustering performance.We also present a new class of clustering algorithms called {em constrained agglomerative algorithms} that combine the features of both partitional and agglomerative algorithms. Our experimental results showed that they consistently lead to better hierarchical solutions than agglomerative or partitional algorithms alone.
Keywords
Description
Related to
Replaces
License
Series/Report Number
Technical Report; 02-022
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Other identifiers
Suggested citation
Zhao, Ying; Karypis, George. (2002). Evaluation of Hierarchical Clustering Algorithms for Document Datasets. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215526.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.