Abstract
Many clustering methods have been proposed in data mining fields, but seldom were focused on the incremental databases. In this paper, we present an incremental algorithm-IFHC that is applicable in periodically incremental environment based on FHC[3]. Not only can FHC and IFHC dispose the data with numeric attributes, but with categorical attributes. Experiment shows that IFHC is faster and more efficient than FHC in update of databases.
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© 2005 Springer-Verlag Berlin Heidelberg
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Dong, Y., Tai, X., Zhao, J. (2005). A Novel Fuzzy-Connectedness-Based Incremental Clustering Algorithm for Large Databases. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_60
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DOI: https://doi.org/10.1007/11539506_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28312-6
Online ISBN: 978-3-540-31830-9
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