Every Machine Learning engineer wants to achieve accurate predictions with their algorithms. Such learning algorithms are generally broken down into two types — supervised and unsupervised. K-means clustering is one of the unsupervised algorithms where the available input data does not have a labeled response.
Let us check out the topics to be covered in this article:
- What is k-means clustering?
- Applications of k-means clustering
- K-Means clustering algorithm
What is meant by the K-means algorithm?
K-Means clustering is an unsupervised learning algorithm. There is no labeled data for this clustering, unlike in supervised learning. K-Means performs the division of objects into clusters that share similarities and are dissimilar to the objects belonging to another cluster.
The term ‘K’ is a number. You need to tell the system how many clusters you need to create. For example, K = 2 refers to two clusters. There is a way of finding out what is the best or optimum value of K for a given data.
For a better understanding of k-means, let’s take an example from cricket. Imagine you received data on a lot of cricket players from all over the world, which gives information on the runs scored by the player and the wickets taken by them in the last ten matches. Based on this information, we need to group the data into two clusters, namely batsmen and bowlers.
K-Means Clustering Algorithm
Let’s say we have x1, x2, x3……… x(n) as our inputs, and we want to split this into K clusters.
The steps to form clusters are:
Step 1: Choose K random points as cluster centers called centroids.
Step 2: Assign each x(i) to the closest cluster by implementing euclidean distance (i.e., calculating its distance to each centroid)
Step 3: Identify new centroids by taking the average of the assigned points.
Step 4: Keep repeating step 2 and step 3 until convergence is achieved
Let’s take a detailed look at each of these steps.
Step 1:
We randomly pick K (centroids). We name them c1,c2,….. ck, and we can say that
Where C is the set of all centroids.
Step 2:
We assign each data point to its nearest center, which is accomplished by calculating the euclidean distance.
Where dist() is the Euclidean distance.
Here, we calculate each x value’s distance from each c value, i.e. the distance between x1-c1, x1-c2, x1-c3, and so on. Then we find which is the lowest value and assign x1 to that particular centroid.
Similarly, we find the minimum distance for x2, x3, etc.
Step 3:
We identify the actual centroid by taking the average of all the points assigned to that cluster.
Where Si is the set of all points assigned to the ith cluster.
It means the original point, which we thought was the centroid, will shift to the new position, which is the actual centroid for each of these groups.
Step 4:
Keep repeating step 2 and step 3 until convergence is achieved.
Applications of K-Means Clustering
K-Means clustering is used in a variety of examples or business cases in real life, like:
- Academic performance
- Diagnostic systems
- Search engines
- Wireless sensor networks
Academic Performance
Based on the scores, students are categorized into grades like A, B, or C.
Diagnostic systems
The medical profession uses k-means in creating smarter medical decision support systems, especially in the treatment of liver ailments.
Search engines
Clustering forms the backbone of search engines. When a search is performed, the search results need to be grouped, and the search engines very often use clustering to do this.
Wireless sensor networks
The clustering algorithm plays the role of finding the cluster heads, which collect all the data in its respective cluster.
So, k-means can be used in many real-life cases, where we have a need to cluster data.
Thank you for reading!!!