2013
DOI: 10.1016/j.eij.2013.02.002
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On determining efficient finite mixture models with compact and essential components for clustering data

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Cited by 3 publications
(3 citation statements)
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“…The ACLNN algorithm was proposed for determining the optimal number of clusters and clustering of input small datasets. The ACLNN algorithm uses the ACL criterion for determining the optimal number of output neurons [16], [17]. The ACL criterion is based on the theory stating that the best cluster structure is composed of balanced, dense, and well-separated clusters that have the least number of parameters to be calculated.…”
Section: Related Workmentioning
confidence: 99%
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“…The ACLNN algorithm was proposed for determining the optimal number of clusters and clustering of input small datasets. The ACLNN algorithm uses the ACL criterion for determining the optimal number of output neurons [16], [17]. The ACL criterion is based on the theory stating that the best cluster structure is composed of balanced, dense, and well-separated clusters that have the least number of parameters to be calculated.…”
Section: Related Workmentioning
confidence: 99%
“…The ACL criterion is based on the theory stating that the best cluster structure is composed of balanced, dense, and well-separated clusters that have the least number of parameters to be calculated. The ACLNN algorithm has been proven to be efficient in identifying the optimal number of clusters and clustering small datasets [16], [17]. Because of its sequential running nature, the ACLNN algorithm has high time complexity and thus consumes a large running time, especially when used with big datasets.…”
Section: Related Workmentioning
confidence: 99%
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