![]() For this, we will repeat the same process of finding a median line. Next, we will reassign each datapoint to the new centroid.To choose the new centroids, we will compute the center of gravity of these centroids, and will find new centroids as below: As we need to find the closest cluster, so we will repeat the process by choosing a new centroid.Let's color them as blue and yellow for clear visualization. Consider the below image:įrom the above image, it is clear that points left side of the line is near to the K1 or blue centroid, and points to the right of the line are close to the yellow centroid. So, we will draw a median between both the centroids. We will compute it by applying some mathematics that we have studied to calculate the distance between two points. Now we will assign each data point of the scatter plot to its closest K-point or centroid.So, here we are selecting the below two points as k points, which are not the part of our dataset. These points can be either the points from the dataset or any other point. We need to choose some random k points or centroid to form the cluster.It means here we will try to group these datasets into two different clusters. Let's take number k of clusters, i.e., K=2, to identify the dataset and to put them into different clusters.The x-y axis scatter plot of these two variables is given below: Let's understand the above steps by considering the visual plots: Step-6: If any reassignment occurs, then go to step-4 else go to FINISH. Step-5: Repeat the third steps, which means reassign each datapoint to the new closest centroid of each cluster. Step-4: Calculate the variance and place a new centroid of each cluster. Step-3: Assign each data point to their closest centroid, which will form the predefined K clusters. (It can be other from the input dataset). Step-2: Select random K points or centroids. Step-1: Select the number K to decide the number of clusters. The working of the K-Means algorithm is explained in the below steps: ![]() The below diagram explains the working of the K-means Clustering Algorithm: How does the K-Means Algorithm Work? Hence each cluster has datapoints with some commonalities, and it is away from other clusters. Those data points which are near to the particular k-center, create a cluster. Assigns each data point to its closest k-center. ![]()
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