Add Count-Min Sketch Algorithm for Efficient Frequency Counting in C, C++, Java and Python

[Aryan-d13]

Title: Add Count-Min Sketch Algorithm for Efficient Frequency Counting

Description: The Count-Min Sketch algorithm is a powerful probabilistic data structure used for efficient frequency counting in large data sets. Currently, our open-source project lacks an implementation of the Count-Min Sketch algorithm, limiting its ability to handle tasks that require approximate frequency counting.

Problem: Tasks such as approximate frequency counting often require significant computational resources and memory when dealing with large data sets. Without the Count-Min Sketch algorithm, our project lacks an efficient method to perform approximate frequency counting, hindering its usability for applications involving large-scale data analysis.

Solution Requested: We propose adding the Count-Min Sketch algorithm to our project. This algorithm provides an approximate counting solution with a small probability of error, making it highly useful for tasks like frequency estimation, heavy hitters identification, and data stream analysis. The implementation should be well-documented, modular, and optimized for performance, ensuring efficient memory usage and accurate frequency estimations.

Alternatives Considered: While there are alternative approaches for frequency counting, such as hash tables and bloom filters, the Count-Min Sketch algorithm stands out for its efficiency and compact representation. It offers a good trade-off between memory usage and accuracy, making it a valuable addition to our project.

Additional Context: Efficient frequency counting is a common requirement in various domains, including network traffic analysis, data streaming, and large-scale analytics. By incorporating the Count-Min Sketch algorithm into our project, we can provide developers with a reliable and efficient tool for approximate frequency counting, enabling them to handle massive data sets with reduced computational and memory overhead.

@Kumar-laxmi Please Assign this to me, I'll start working on it ASAP.

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