Is your feature request related to a problem? Please describe.
The current open source lacks an implementation of the K-means++ algorithm, which is a popular and effective initialization technique for the K-means clustering algorithm. The absence of this algorithm limits the project's ability to perform more efficient and accurate clustering on datasets.
Describe the solution you'd like
I would like to contribute to this open source project by adding the K-means++ algorithm. K-means++ improves the initial centroid selection in K-means clustering, resulting in better convergence and avoiding suboptimal solutions. By including this algorithm, the project will offer an enhanced and more robust clustering solution.
Describe alternatives you've considered
There are alternative initialization techniques for K-means clustering, such as random initialization and the Forgy method. However, K-means++ has been widely recognized as a superior approach due to its ability to select more representative initial centroids, resulting in improved clustering performance.
Additional context
I have experience with the K-means++ algorithm and believe it would be a valuable addition to the project. I am familiar with the implementation details and can ensure that the code adheres to the project's coding standards and guidelines. Adding K-means++ will enhance the project's functionality and provide users with a more powerful clustering tool.
@Kumar-laxmi, I would be happy to help you implement this algorithm in the repository if you would like.
Kumar-laxmi
commented
1 year ago
Is your feature request related to a problem? Please describe. The current open source lacks an implementation of the K-means++ algorithm, which is a popular and effective initialization technique for the K-means clustering algorithm. The absence of this algorithm limits the project's ability to perform more efficient and accurate clustering on datasets.
Describe the solution you'd like I would like to contribute to this open source project by adding the K-means++ algorithm. K-means++ improves the initial centroid selection in K-means clustering, resulting in better convergence and avoiding suboptimal solutions. By including this algorithm, the project will offer an enhanced and more robust clustering solution.
Describe alternatives you've considered There are alternative initialization techniques for K-means clustering, such as random initialization and the Forgy method. However, K-means++ has been widely recognized as a superior approach due to its ability to select more representative initial centroids, resulting in improved clustering performance.
Additional context I have experience with the K-means++ algorithm and believe it would be a valuable addition to the project. I am familiar with the implementation details and can ensure that the code adheres to the project's coding standards and guidelines. Adding K-means++ will enhance the project's functionality and provide users with a more powerful clustering tool. @Kumar-laxmi, I would be happy to help you implement this algorithm in the repository if you would like.