Abstract
The most common way to organize and label documents is to group similar documents into clusters. Normally, the assumed number of clusters may be unreliable since the nature of the grouping structures among the data is unknown before processing and thus the partitioning methods would not predict the structures of the data very well. Hierarchical clustering has been chosen to solve this problem by which they provide data-views at different levels of abstraction, making them ideal for people to visualize the concepts generated and interactively explore large document collections. The appropriate method of combining two different clusters to form a single cluster needs affects the quality of clusters produced. In order to perform this task, various distance methods will be studied in order to cluster documents by using the hierarchical agglomerative clustering. Clusters very often include sub-clusters, and the hierarchical structure is indeed a natural constraint on the underlying application domain. In order to manage and organize documents effectively, similar documents will be merged to form clusters. Each document is represented by one or more concepts. In this paper, concepts that characterize English documents will be generated by using the hierarchical agglomerative clustering. One of the advantages of using hierarchical clustering is that the overlapping clusters can be formed and concepts can be generated based on the contents of each cluster. The quality of clusters produced is also investigated by using different distance measures.
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Acknowledgments
This work has been supported by the Long Term Research Grant Scheme (LRGS) project funded by the Ministry of Higher Education (MoHE), Malaysia under Grants No. LRGS/TD/2011/UiTM/ICT/04.
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Alfred, R., Fun, T.S., Tahir, A., On, C.K., Anthony, P. (2014). Concepts Labeling of Document Clusters Using a Hierarchical Agglomerative Clustering (HAC) Technique. In: Uden, L., Wang, L., Corchado RodrÃguez, J., Yang, HC., Ting, IH. (eds) The 8th International Conference on Knowledge Management in Organizations. Springer Proceedings in Complexity. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7287-8_21
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DOI: https://doi.org/10.1007/978-94-007-7287-8_21
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