Abstract
Many clustering methods are not suitable for high-dimensional data mining applications because of the so-called ‘curse of dimensionality’ and the limitation of available memory. In this paper, we propose a new cell-based clustering method for the high-dimensional data mining applications. The proposed clustering method provides efficient cell creation and cell insertion algorithms using a space-partitioning technique, as well as makes use of a filtering-based index structure using an approximation technique. In addition, we compare the performance of our cell-based clustering method with the CLIQUE method which is well known as an efficient grid-based clustering method for high-dimensional data. The experimental results show that our clustering method achieves better performance on cluster construction time and retrieval time.
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Chang, JW. (2005). A New Cell-Based Clustering Method for High-Dimensional Data Mining Applications. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552413_56
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DOI: https://doi.org/10.1007/11552413_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28894-7
Online ISBN: 978-3-540-31983-2
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