K-means clustering is a very famous and powerful unsupervised machine learning algorithm. It is used to solve many complex unsupervised machine learning problems.
What is unsupervised learning?
Unsupervised learning is where you train a machine learning algorithm, but you don’t give it the answer to the problem. In Unsupervised Learning, the machine uses unlabeled data and learns on itself without any supervision. The machine tries to find a pattern in the unlabeled data and gives a response.
Types of Unsupervised Learning Algorithm:
The unsupervised learning algorithm can be further categorized into two types of problems:
- Clustering: Clustering is a method of grouping the objects into clusters such that objects with most similarities remains into a group and has less or no similarities with the objects of another group. Cluster analysis finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities.
- Association: An association rule is an unsupervised learning method which is used for finding the relationships between variables in the large database. It determines the set of items that occurs together in the dataset.
Now let’s move on to our main topic i.e K-Mean clustering.
How Does the K-means clustering algorithm work?
k-means clustering tries to group similar kinds of items in form of clusters. It finds the similarity between the items and groups them into the clusters. K-means clustering algorithm works in three steps. Let’s see what are these three steps.
- Select the k values.
- Initialize the centroids.
- Select the group and find the average.
- Figure 1 shows the representation of data of two different items. the first item has shown in blue color and the second item has shown in red color. Here I am choosing the value of K randomly as 2. There are different methods by which we can choose the right k values.
- In figure 2, Join the two selected points. Now to find out centroid, we will draw a perpendicular line to that line. The points will move to their centroid. If you will notice there, then you will see that some of the red points are now moved to the blue points. Now, these points belong to the group of blue color items.
- The same process will continue in figure 3. we will join the two points and draw a perpendicular line to that and find out the centroid. Now the two points will move to its centroid and again some of the red points get converted to blue points.
- The same process is happening in figure 4. This process will be continued until and unless we get two completely different clusters of these groups.
How to choose the value of “K number of clusters” in K-means Clustering?
One of the most challenging tasks in this clustering algorithm is to choose the right values of k. There are some different ways to find the optimal number of clusters, but here we are discussing the most appropriate method to find the number of clusters or value of K. The method is given below:
Elbow Method
Elbow is one of the most famous methods by which you can select the right value of k and boost your model performance. We also perform the hyperparameter tuning to chose the best value of k. Let us see how this elbow method works.
It is an empirical method to find out the best value of k. it picks up the range of values and takes the best among them. It calculates the sum of the square of the points and calculates the average distance.
When the value of k is 1, the within-cluster sum of the square will be high. As the value of k increases, the within-cluster sum of square value will decrease.
Finally, we will plot a graph between k-values and the within-cluster sum of the square to get the k value. we will examine the graph carefully. At some point, our graph will decrease abruptly. That point will be considered as a value of k.
Use cases for k-means Algorithm:
1. Cyber-profiling criminals
cyber-profiling is the process of collecting data from individuals and groups to identify significant co-relations. the idea of cyber profiling is derived from criminal profiles, which provide information on the investigation division to classify the types of criminals who were at the crime scene.
2. Call record detail analysis
A call detail record (cdr) is the information captured by telecom companies during the call, sms, and internet activity of a customer. this information provides greater insights about the customer’s needs when used with customer demographics. You will understand how you can cluster customer activities for 24 hours by using the unsupervised k-means clustering algorithm. it is used to understand segments of customers with respect to their usage by hours.
3. Identifying crime localities
With data related to crimes available in specific localities in a city, the category of crime, the area of the crime, and the association between the two can give quality insight into crime-prone areas within a city or a locality.
4. Insurance fraud detection
Deviation and fraud would be found in most of the economic activities. More roles in inspecting a payment case lead to more risks of fraud. One of the ways of detecting fraud in reported damages and losses is to use information remained from former detected fraudulent cases. K-Means clustering can detect fraud patterns in automobile body insurance and third-party insurance.
I hope you find this blog imformative. Thanks for reading :)