Abstract
Recently, there have been many opportunities to acquire text information as the quantity of electronic information increases. Data classification or clustering methods are widely adapted in order to acquire various information effectively from an enormous active text data set. However, ordinal clustering methods connect texts and many texts are concentrated into a single cluster so that we cannot see various information.
In this study, we propose a recursive clustering method to avoid such bias by integrating a set of texts, included in a cluster, into a single text. An interface that we can comprehend a result of clustering intuitively and can explore information is required to grasp an overview of data and to be led to a new idea. According to the experimental results, the proposed method could construct clusters that are not biased. Test subjects could find information widely by using a map visualizing clustering results.
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Sunayama, W., Hamaoka, S., Okuda, K. (2013). Map Interface for a Text Data Set by Recursive Clustering. In: Ohsawa, Y., Abe, A. (eds) Advances in Chance Discovery. Studies in Computational Intelligence, vol 423. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30114-8_5
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DOI: https://doi.org/10.1007/978-3-642-30114-8_5
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