K Means Clustering
What is k-means?
clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters. the goal of the k-means algorithm is to find groups in the data, with the number of groups represented by the variable k. The algorithm works iteratively to assign each data point to one of the k groups based on the features that are provided. In the reference image below, k=2, and there are two clusters identified from the source dataset.
The outputs of executing a k-means on a dataset are:
- k centroids: centroids for each of the k clusters identified from the dataset.
- complete dataset labeled to ensure each data point is assigned to one of the clusters.
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.
Where can I apply k-means?
k-means can typically be applied to data that has a smaller number of dimensions, is numeric, and is continuous. Think of a scenario in which you want to make groups of similar things from a randomly distributed collection of things; k-means is very suitable for such scenarios.
Applications of Clustering in Real-World Scenarios
Clustering is a widely used technique in the industry. It is actually being used in almost every domain, ranging from banking to recommendation engines, document clustering to image segmentation, and security domain too.
Customer Segmentation
We covered this earlier — one of the most common applications of clustering is customer segmentation. And it isn’t just limited to banking. This strategy is across functions, including telecom, e-commerce, sports, advertising, sales, etc.
Document Clustering
This is another common application of clustering. Let’s say you have multiple documents and you need to cluster similar documents together. Clustering helps us group these documents such that similar documents are in the same clusters.
Image Segmentation
We can also use clustering to perform image segmentation. Here, we try to club similar pixels in the image together. We can apply clustering to create clusters having similar pixels in the same group.
You can refer to this article to see how we can make use of clustering for image segmentation tasks.
Recommendation Engines
Clustering can also be used in recommendation engines. Let’s say you want to recommend songs to your friends. You can look at the songs liked by that person and then use clustering to find similar songs and finally recommend the most similar songs.
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.
