Abstract
This paper proposes a novel approach for unsupervised classification of variable length sequence data using a concept inspired from the Particle Swarm Optimization and rational kernel. The approach uses the distance estimated by the rational kernel as a similarity measure used for clustering the particles. It does not require the normalization of the data sequences into fixed size vectors. Each data sequence has a corresponding particle which moves in the parameter space towards other particles with the similar fitness value. Velocity factor which is used in updating particle position is influenced by the rational distance below a specified threshold. Experimental results display the robustness of proposed algorithm. Misclassification error for clustering the particles into different classes is provided in the results section.
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Raveendran, S., Vinodchandra, S.S. (2016). An Approach Using Particle Swarm Optimization and Rational Kernel for Variable Length Data Sequence Optimization. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_40
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DOI: https://doi.org/10.1007/978-3-319-41000-5_40
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