Clustering is a crucial step in descriptive statistics and data mining. It is used in many different fields of work, including data categorization and image processing, and has been the subject of much study by many different academics. We present I-BIRCH, an improved balanced iterative reducing and clustering technique that makes use of hierarchies. It works well with massive datasets and is an unsupervised data mining technique. The algorithm begins by clustering data points with a single dimension, and then it moves on to cluster data points with many dimensions in order to get the optimal clustering with a single view of the data. The "noise" (data points that do not form part of the underlying pattern) is something it can manage. Clustering calculations take O(n2) time and use a distance matrix that is O(n2) huge. When mining complicated or massive datasets, this kind of grouping is a necessary component. When there is information about the heart, such an ID, a nam