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Erschienen in:
2005 | OriginalPaper | Buchkapitel
A Fast Fuzzy Clustering Algorithm for Large-Scale Datasets
verfasst von : Lukui Shi, Pilian He
Erschienen in: Advanced Data Mining and Applications
Verlag: Springer Berlin Heidelberg
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The transitive closure method is one of the most frequently used fuzzy clustering techniques. It has
O
(
n
3
log
2
n
) time complexity and
O
(
n
2
) space complexity for matrix compositions while building transitive closures. These drawbacks limit its further applications to large-scale databases. In this paper, we proposed a fast fuzzy clustering algorithm to avoid matrix multiplications and gave a principle, where the clustering results were directly obtained from the
λ
-cut of the fuzzy similar relation of objects. Moreover, it was dispensable to compute and store the similar matrix of objects beforehand. The time complexity of the presented algorithm is
O
(
n
2
) at most and the space complexity is
O
(1). Theoretical analysis and experiments demonstrate that although the new algorithm is equivalent to the transitive closure method, the former is more suitable to treat large-scale datasets because of its high computing efficiency.