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Title:
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A NEW SPACE-PARTITIONING CLUSTERING METHOD FOR HIGH-DIMENSIONAL DATA MINING |
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Author(s):
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Jaewoo Chang , Ahreum Kim |
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ISBN:
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978-972-8924-56-0 |
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Editors:
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Nuno Guimarães and Pedro Isaías |
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Year:
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2008 |
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Edition:
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Single |
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Keywords:
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Clustering method, high-dimensional data mining, filtering-based index structure |
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Type:
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Full Paper |
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First Page:
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251 |
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Last Page:
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258 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Many clustering methods are not suitable for high-dimensional data mining because of the so-called curse of
dimensionality and the limitation of available memory. In this paper, we propose a new space-partitioning clustering
method for the high-dimensional data mining. 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 clustering method with the CLIQUE
method which is well known as an efficient clustering method for high-dimensional data mining. The experimental
results show that our clustering method achieves better performance on cluster construction time and retrieval time than
the CLIQUE. |
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