Ideal/balance point clustering algorithm

Generally clustering is one of the largest used ML techniques to group data points of similar type together. Mostly while grouping data points based upon few popular traditional clustering algorithms like KNN, K-means etc. only the magnitudes of those data points are considered. The deviation of those data points from a particular point said to be the center point or the balance point of the entire dataset is not considered that much, but Here in this Ideal Balance Point clustering algorithm the degree of deviation of all the data points from the balance point is primarily considered. By using such a methodology, we may be able to form clusters of data points based on their degrees of deviation from the Ideal Balance Point of the entire dataset. The Ideal Balance Point of the dataset is actually the centroid of the plane on which the datapoints are distributed. Here in the Ideal Balance Point clustering algorithm the user may also manually feed-in the Ideal Balance Point for the dataset based on the requirement or as per the practical idealness of the circumstance and nature of the dataset. Generally, this idea is derived from the theory that an object has all its’ mass equally distributed on the centroid. Hence the plane formed by the distribution of the data points is considered as an object and the centroid of it is assumed to be the point where the entire data points are directed into, but the ideal balance point for the data may also differ as per real life scenario, thus the feature of feeding in the balance point is also given in this Ideal Balance Point clustering algorithm.