Healthcare decision making using k-means clustering technique

Data mining is becoming more capable of uncovering data from data repositories and warehouses in today's world. It is used to separate important facts and make critical connections between variables stored in a large informational index. K-means implies grouping is a common bunching calculation that is based on the information segment. The technique for grouping calculations will have a direct impact on the bunching result. Clustering is one of the most important deliberate procedures in facts or data and information mining. The purpose of this paper is to highlight the importance of Data Mining and K-means Clustering in decision making and how they can be used to support it. I use sample data to demonstrate how to use a data mining tool to implement the k-means clustering algorithm and how data mining and clustering can help with decision making.