Abstract
In this paper, we propose new ideas around the concepts of knowledge warehousing and mining. More precisely, we focus on the mining part and develop original approaches for incremental clustering based on k-means for knowledge bases. Instead of addressing the prohibitive amounts of knowledge, the latter is gradually exploited by packets in order to reduce the problem complexity. We introduce original algorithms named ICPK/k-means for Incremental Clustering by Packets of Knowledge, ICPKG/k-means for Incremental Algorithm by Packets of Knowledge and Grouping of clusters for determining the number of desired clusters, LICPK/k-means for Learning Incremental Clustering by Packets of Knowledge and LIGPKG/k-means for Learning Incremental Clustering by Packets of Knowledge and Grouping of clusters for handling the clustering of large amount of knowledge. Experimental results prove the effectiveness of our algorithms.
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© 2012 Springer-Verlag Berlin Heidelberg
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Drias, H., Aouichat, A., Boutorh, A. (2012). Towards Incremental Knowledge Warehousing and Mining. In: Omatu, S., De Paz Santana, J., González, S., Molina, J., Bernardos, A., RodrÃguez, J. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28765-7_60
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DOI: https://doi.org/10.1007/978-3-642-28765-7_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28764-0
Online ISBN: 978-3-642-28765-7
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