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BIRCH: an efficient data clustering method for very large databases
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Nonlinear component analysis as a kernel eigenvalue problem
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Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
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A survey of kernel and spectral methods for clustering
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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Information Sciences: an International Journal
A novel ant-based clustering algorithm using Renyi entropy
Applied Soft Computing
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Information Sciences: an International Journal
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Journal of Information Science
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Information Sciences: an International Journal
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Information Sciences: an International Journal
Deterministic walks with choice
Discrete Applied Mathematics
Information Sciences: an International Journal
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A novel ant-based clustering algorithm integrated with the kernel (ACK) method is proposed. There are two aspects to the integration. First, kernel principal component analysis (KPCA) is applied to modify the random projection of objects when the algorithm is run initially. This projection can create rough clusters and improve the algorithm's efficiency. Second, ant-based clustering is performed in the feature space rather than in the input space. The distance between the objects in the feature space, which is calculated by the kernel function of the object vectors in the input space, is applied as a similarity measure. The algorithm uses an ant movement model in which each object is viewed as an ant. The ant determines its movement according to the fitness of its local neighbourhood. The proposed algorithm incorporates the merits of kernel-based clustering into ant-based clustering. Comparisons with other classic algorithms using several synthetic and real datasets demonstrate that ACK method exhibits high performance in terms of efficiency and clustering quality.