Abstract
As the online business is being exponentially advanced from time to time, analyzing the data on the fly is the need of the hour and getting high attention from the researchers. The proliferation of a variety of data that has been generated from different kinds of devices needs to have a practically efficient algorithm for the data stream analysis. However, most of the existing data mining algorithms are widely used for the analysis of the stream of data as well which are better for static data mining and not as efficient for the data stream. Thus, to overcome this limitation, this work has proposed an adaptive classifier (algorithm) for the analysis of data stream instantly. The proposed adaptive classifier uses extreme learning machine along with the product’s attributes-based rule database to identify the popularity of the product.
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Raja, M.A.M., Swamynathan, S., Sumitha, T. (2022). Adaptive Classifier Using Extreme Learning Machine for Classifying Twitter Data Streams. In: Nayak, J., Behera, H., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Data Mining. Smart Innovation, Systems and Technologies, vol 281. Springer, Singapore. https://doi.org/10.1007/978-981-16-9447-9_28
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DOI: https://doi.org/10.1007/978-981-16-9447-9_28
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