Abstract
Humongous amount of data is continuously generated by thousands of data sources, which simultaneously send data records that include a wide variety of elements of mixed types such as electronic purchases, information from social networks. They should be processed sequentially and incrementally over flexible time windows and then used for different analyzes. On account on these new instances that includes new attributes which have to be learned as development proceeds, called data stream, this paper tackles the incremental attribute learning task for mixed data using k-prototypes algorithm. Firstly, we propose a novel Incremental k-prototypes algorithm based on the merge technique to ensure the attribute learning task. Subsequently, we present the experiments to evaluate our new method using several real mixed data sets. The results show an improvement of the performance of the k-prototypes algorithm used for incremental attribute learning where the proposed Incremental k-prototypes method gives better results regarding the k-prototypes algorithms.
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Gorrab, S., Rejab, F.B. (2021). IK-prototypes: Incremental Mixed Attribute Learning Based on K-Prototypes Algorithm, a New Method. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_81
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DOI: https://doi.org/10.1007/978-3-030-71187-0_81
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