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A new fast fuzzy partitioning algorithm
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DOI：10.1016/j.eswa.2015.12.034.png)
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摘要
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In this paper, a new fast incremental fuzzy partitioning algorithm able to find either a fuzzy globally optimal partition or a fuzzy locally optimal partition of the set A subset of R-n close to the global one is proposed. This is the main impact of the paper, which could have an important role in applied research. Since fuzzy k-optimal partitions with k = 2, 3,..., k(max) clusters are determined successively in the algorithm, it is possible to calculate corresponding validity indices for every obtained partition. The number k(max) is defined in such a way that the objective function value of optimal partition with km, clusters is relatively very close to the objective function value of optimal partition with (k(max)-1) clusters. Before clustering, the data are normalized and afterwards several validity indices are applied to partitions of the normalized data. Very simple relationships between used validity indices on normalized and original data are given as well. Hence, the proposed algorithm is able to find optimal partitions with the most appropriate number of clusters. The algorithm is tested on numerous synthetic data sets and several real data sets from the UCI data repository. (C) 2016 Elsevier Ltd. All rights reserved.
Keyword:
Fuzzy clustering
Fuzzy c-means
Fuzzy locally optimal partition
Fuzzy globally optimal partition
DIRECT
Incremental algorithm
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7.5
论文数:
2.9W
被引数:
10.2W
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