subroto08820 / GoldSaxKmeansClustering

Modelled, Architected and designed by Vance King Saxbe. A. with the geeks from GoldSax Consulting, GoldSax Money, GoldSax Treasury, GoldSax Finance, GoldSax Banking and GoldSax Technologies email @vsaxbe@yahoo.com. Development teams from Power Dominion Enterprise, Precieux Consulting. This Engagement sponsored by GoldSax Foundation, GoldSax Group and executed by GoldSax Manager. Clustering is a technique used in unsupervised learning. With unsupervised learning you don’t know what you’re looking for, that is, there are no target variables. Clustering groups data points together, with similar data points in one cluster and dissimilar points in a different group. A number of different measurements can be used to measure similarity. One widely used clustering algorithm is k-means, where k is a user-specified number of clusters to create. The k-means clustering algorithm starts with k-random cluster centers known as centroids. Next, the algorithm computes the distance from every point to the cluster centers. Each point is assigned to the closest cluster center. The cluster centers are then recalculated based on the new points in the cluster. This process is repeated until the cluster centers no longer move. This simple algorithm is quite effective but is sensitive to the initial cluster placement.