Abstract
The data partition is a method which makes the processing of the database server’s easy. It is like the clustering of the similar type of data files in an order so that the searching becomes easy. The data may be structured or unstructured. This paper focuses on the development of a unique data partition method which utilizes column integrated data. The proposed algorithm also utilizes Natural Computing Optimization inspired Genetic Algorithm (GA) for the improvisation of the partitioned data structure. The optimized set is cross validated utilizing Artificial Neural Network. This results into high values of evaluation parameters. The evaluation of the proposed algorithm is done using Precision, Recall and F-measure.
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Kaur, K., Laxmi, V. (2020). A Novel Method of Data Partitioning Using Genetic Algorithm Work Load Driven Approach Utilizing Machine Learning. In: Mallick, P., Pattnaik, P., Panda, A., Balas, V. (eds) Cognitive Computing in Human Cognition. Learning and Analytics in Intelligent Systems, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-030-48118-6_5
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