Abstract
This paper presents an effective elastic net-based clustering algorithm for complex and non-linearly separable data. The basic idea of the proposed algorithm is simple and can be summarized into two steps: (1) assign patterns to groups based on the attraction and tension between the elastic nodes in a ring and neighbors of the patterns and (2) merge the groups based on the distance between the elastic nodes. To evaluate the performance of the proposed method, we compare it with several state-of-the-art clustering methods in solving the data clustering problem. The simulation results show that the proposed algorithm can provide much better results than the other clustering algorithms compared in terms of the accuracy rate. The results also show that the proposed algorithm works well for complex datasets, especially non-linearly separable data.
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Tsai, CW., Tung, CH., Chiang, MC. (2013). An Elastic Net Clustering Algorithm for Non-linearly Separable Data. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36546-1_12
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DOI: https://doi.org/10.1007/978-3-642-36546-1_12
Publisher Name: Springer, Berlin, Heidelberg
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