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- W2120612963 abstract "Text clustering has been recognized as an important component in Web-based applications. Clustering data on a hierarchical structure enables exploring data on different levels of granularity, providing a more intuitive view that is close to the way humans view the world. Self-organizing map (SOM) based models have been found to have certain advantages for clustering sizeable text data. However, current existing approaches lack in providing an adaptive hierarchical structure within in a single model. This paper proposes an unsupervised hierarchical clustering approach based on the growing self-organizing map (GSOM). By utilizing GSOM's spread factor, our approach offers an adaptive architecture with the capability of detecting necessary layers to form a hierarchy, avoiding a number of issues that a traditional top-down or bottom-up hierarchical clustering approach often encounter. Experiment has shown that this approach has the potential for efficiently clustering heterogeneous text data" @default.
- W2120612963 created "2016-06-24" @default.
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- W2120612963 date "2006-05-25" @default.
- W2120612963 modified "2023-09-27" @default.
- W2120612963 title "Building an Adaptive Hierarchy of Clusters for Text Data" @default.
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- W2120612963 doi "https://doi.org/10.1109/cimca.2005.1631437" @default.
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