NIOJS: A Novel Intelligent Model Based on Optimal Jumps for Creating Data Sampling from Big Dataset
Keywords:
Big data sampling, Cluster sampling, DBSCAN, NIOJS , Samples, Optimal JumpAbstract
The pervasiveness of big data has revolutionized the landscape of information technology (IT), offering a wealth of insights and opportunities for various sectors, including healthcare, education, and the Internet of Things (IoT). However, the sheer volume and complexity of big data pose challenges in extracting meaningful knowledge. To address this, we propose a novel model for optimal sample selection, enabling efficient extraction of representative subsets from big data. The proposed model, based on optimal jumps, dynamically adapts the clustering process to enhance the efficiency of data sampling. We employ the Adjusted Rand Index (ARI) to evaluate the similarity between clusters and guide the selection of new data in each iteration This model holds the potential to significantly enhance the utilization of big data while reducing computational demands. The proposed could run on big datasets and the samples taken represents the dataset.
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