Abstract
A common issue in cluster analysis is that there is no single correct answer to the number of clusters, since cluster analysis involves human subjective judgement. Interactive visualization is one of the methods where users can decide a proper clustering parameters. In this paper, a new clustering approach called CDCS (Categorical Data Clustering with Subjective factors) is introduced, where a visualization tool for clustered categorical data is developed such that the result of adjusting parameters is instantly reflected. The experiment shows that CDCS generates high quality clusters compared to other typical algorithms.
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© 2004 Springer-Verlag Berlin Heidelberg
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Chang, CH., Ding, ZK. (2004). Categorical Data Visualization and Clustering Using Subjective Factors. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2004. Lecture Notes in Computer Science, vol 3181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30076-2_23
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DOI: https://doi.org/10.1007/978-3-540-30076-2_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22937-7
Online ISBN: 978-3-540-30076-2
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