UNIT – 2: UNSUPERVISED LEARNING ALGORITHM
K-MEANS CLUSTERING ALGORITHM
K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into
different clusters. Here K defines the number of pre-defined clusters that need to be created in the
process, as if K=2, there will be two clusters, and for K=3, there will be three clusters, and so on.
It is an iterative algorithm that divides the unlabeled dataset into k different clusters in such a way
that each dataset belongs only one group that has similar properties.
The algorithm takes the unlabeled dataset as input, divides the dataset into k-number of clusters, and
repeats the process until it does not find the best clusters. The value of k should be predetermined in
this algorithm. The k-means clustering algorithm mainly performs two tasks:
➢ Determines the best value for K center points or centroids by an iterative process.
➢ Assigns each data point to its closest k-center. Those data points which are near to the
particular k-center, create a cluster.
Hence each cluster has datapoints with some commonalities, and it is away from other clusters.
How does the K-Means Algorithm Work? The working of the K-Means algorithm is explained in
the below steps:
Step-1: Select the number K to decide the number of clusters.
Step-2: Select random K points or centroids. (It can be other from the input dataset).
Step-3: Assign each data point to their closest centroid, which will form the predefined K clusters.
Step-4: Calculate the variance and place a new centroid of each cluster.
Step-5: Repeat the third steps, which means reassign each datapoint to the new closest centroid of
each cluster.
Step-6: If any reassignment occurs, then go to step-4 else go to FINISH.
Step-7: The model is ready.
Suppose we have two variables M1 and M2. The x-y axis scatter plot of these two variables is given
below:
, ➢ Let's take number k of clusters, i.e., K=2, to identify the dataset and to put them into different
clusters. It means here we will try to group these datasets into two different clusters.
➢ We need to choose some random k points or centroid to form the cluster. These points can be
either the points from the dataset or any other point. So, here we are selecting the below two
points as k points, which are not the part of our dataset.
➢ Now we will assign each data point of the scatter plot to its closest K-point or centroid. We
will compute it by applying some mathematics that we have studied to calculate the distance
between two points. So, we will draw a median between both the centroids.
From 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. Let's color them as blue and yellow for
clear visualization.
, ➢ As we need to find the closest cluster, so we will repeat the process by choosing a new
centroid. To choose the new centroids, we will compute the center of gravity of these
centroids, and will find new centroids as below:
➢ Next, we will reassign each datapoint to the new centroid. For this, we will repeat the same
process of finding a median line. The median will be like below image:
From the above image, we can see, one yellow point is on the left side of the line, and two blue points
are right to the line. So, these three points will be assigned to new centroids.
K-MEANS CLUSTERING ALGORITHM
K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into
different clusters. Here K defines the number of pre-defined clusters that need to be created in the
process, as if K=2, there will be two clusters, and for K=3, there will be three clusters, and so on.
It is an iterative algorithm that divides the unlabeled dataset into k different clusters in such a way
that each dataset belongs only one group that has similar properties.
The algorithm takes the unlabeled dataset as input, divides the dataset into k-number of clusters, and
repeats the process until it does not find the best clusters. The value of k should be predetermined in
this algorithm. The k-means clustering algorithm mainly performs two tasks:
➢ Determines the best value for K center points or centroids by an iterative process.
➢ Assigns each data point to its closest k-center. Those data points which are near to the
particular k-center, create a cluster.
Hence each cluster has datapoints with some commonalities, and it is away from other clusters.
How does the K-Means Algorithm Work? The working of the K-Means algorithm is explained in
the below steps:
Step-1: Select the number K to decide the number of clusters.
Step-2: Select random K points or centroids. (It can be other from the input dataset).
Step-3: Assign each data point to their closest centroid, which will form the predefined K clusters.
Step-4: Calculate the variance and place a new centroid of each cluster.
Step-5: Repeat the third steps, which means reassign each datapoint to the new closest centroid of
each cluster.
Step-6: If any reassignment occurs, then go to step-4 else go to FINISH.
Step-7: The model is ready.
Suppose we have two variables M1 and M2. The x-y axis scatter plot of these two variables is given
below:
, ➢ Let's take number k of clusters, i.e., K=2, to identify the dataset and to put them into different
clusters. It means here we will try to group these datasets into two different clusters.
➢ We need to choose some random k points or centroid to form the cluster. These points can be
either the points from the dataset or any other point. So, here we are selecting the below two
points as k points, which are not the part of our dataset.
➢ Now we will assign each data point of the scatter plot to its closest K-point or centroid. We
will compute it by applying some mathematics that we have studied to calculate the distance
between two points. So, we will draw a median between both the centroids.
From 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. Let's color them as blue and yellow for
clear visualization.
, ➢ As we need to find the closest cluster, so we will repeat the process by choosing a new
centroid. To choose the new centroids, we will compute the center of gravity of these
centroids, and will find new centroids as below:
➢ Next, we will reassign each datapoint to the new centroid. For this, we will repeat the same
process of finding a median line. The median will be like below image:
From the above image, we can see, one yellow point is on the left side of the line, and two blue points
are right to the line. So, these three points will be assigned to new centroids.
