Mine Blood Donors Information through Improved K-Means Clustering

TL;DR

This paper presents an improved K-means clustering algorithm that enhances initial centroid selection based on data distribution, leading to more accurate blood donor clustering with reduced computation time.

Contribution

The paper introduces a novel method for initializing centroids in K-means based on data distribution, improving clustering efficiency and accuracy.

Findings

Improved clustering accuracy over traditional K-means.

Reduced computation time for clustering process.

Effective organization of blood donor data.

Abstract

The number of accidents and health diseases which are increasing at an alarming rate are resulting in a huge increase in the demand for blood. There is a necessity for the organized analysis of the blood donor database or blood banks repositories. Clustering analysis is one of the data mining applications and K-means clustering algorithm is the fundamental algorithm for modern clustering techniques. K-means clustering algorithm is traditional approach and iterative algorithm. At every iteration, it attempts to find the distance from the centroid of each cluster to each and every data point. This paper gives the improvement to the original k-means algorithm by improving the initial centroids with distribution of data. Results and discussions show that improved K-means algorithm produces accurate clusters in less computation time to find the donors information.