This paper puts forward a novel approach in big data processing and it is aimed at cutting computational time and enhancing classification accuracy. The research focuses on the relevance and significance of hybrid algorithms, specifically combining the Ball Tree and Weighted k Nearest Neighbors (k-NN) algorithms. The objective of this study is to address the limitations of traditional algorithms by reducing computational time while improving classification accuracy. The methodology employed in this research is the constructive research method, which allows for the development and evaluation of new algorithms. This methodology was chosen as it facilitates the creation of innovative approaches to tackle the challenges of big data processing. Experimental results demonstrate that the proposed hybrid algorithm yields promising outcomes. When classifying the MNIST dataset, the algorithm achieved an accuracy rate of 97%, misclassifying only 256 out of 10,000 images. The harmonic mean between precision and recall was found to be 0.999716, indicating a high level of performance. Notably, the computational time required for classification was significantly shorter compared to traditional classification techniques. Overall, the hybrid algorithm combining the Ball Tree and Weighted k-NN proved to be an effective solution for big data processing. By reducing computational time and enhancing accuracy, it presents a valuable contribution to the field. This research opens avenues for further exploration and application of hybrid algorithms in various domains where efficient and accurate big data processing is crucial.

Big Data, Ball Tree Algorithm, Classification, Weighted K-Nearest Neighbours (WKNN), Hybrid Algorithm, K- Nearest Neighbours, MNIST Dataset.

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