A Two Stage Clustering Method Combining Self-Organizing Maps and Ant K-Means
نویسندگان
چکیده
This paper proposes a clustering method SOMAK, which is composed by Self-Organizing Maps (SOM) followed by the Ant K-means (AK) algorithm. The aim of this method is not to find an optimal clustering for the data, but to obtain a view about the structure of data clusters. SOM is an Artificial Neural Network, which has one of its characteristics the nonlinear projection. AK is a meta-heuristic approach for solving hard combinatorial optimization problems based on Ant Colony Optimization (ACO). The SOMAK has a good performance when compared with some clustering techniques and reduces the computational time.
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تاریخ انتشار 2009