Both supervised and unsupervised machine learning techniques have a wide range of applications. The supervised machine learning techniques usually rely on labeled data for the purpose of training models. On the other hand, unsupervised learning techniques are used for detecting hidden patterns in unlabeled data types. One of the most important unsupervised machine learning techniques is called clustering. The technique of clustering has a wide range of applications. As such, it is one of the core components of all big data courses. Clustering finds its application in advanced analytics domains of marketing, economics, and finance. One of the most important methods of clustering is called K-means clustering. Let’s take a look at this method in deeper detail.
K means clustering
When we have a large data set with corresponding attributes, we can effectively use k-means clustering. We choose a value of k to group the data into K clusters. The clusters are formed in accordance with their proximity to the identified center. We use the mean or the average value for the calculation of the center of the overall grouping. After the clusters have been identified, the next stage in the process is the assigning of labels to each cluster. In this way, clustering enables us to unravel various types of hidden structures in the given data set. Clustering proves to be one of the most important methods for advanced analytics and even decision sciences. K-means clustering technique finds applications in customer segmentation, image processing, and medical diagnosis.
The clustering methodology
The clustering methodology is used for the formation of K clusters from a group of n number of objects with X attributes. The clustering algorithm that we apply here relies on four important steps. The first important step is the choice of the value of k. We usually zero in on the value of k by the hit and trial method. The second step involves the computation of the distance of each data point from the center. We may use the euclidean distance formula to determine the distance between two points. The third step involves the computation of the centroid as well as the center of mass of the cluster that has been defined. The fourth step is simply an iterative process in which we repeat the initial steps to arrive at an answer. It needs to be noted at this point in time that general practice is to assign the points in the data set to the closest centroid for convenience.
Medical applications
One of the most important medical applications of the process of clustering is used to segregate patients on the basis of factors like age, height, weight, and other important attributes. Let us understand this with the help of an example. The vaccination drive that was launched required the clustering of people on the basis of their age and even previous infection. This is where K means clustering proved to be an effective method for prioritizing the population for the purpose of vaccination. Other important applications include the classification of plants and animals on the basis of predefined attributes.
Customer segmentation
The process of customer segmentation can be effectively accomplished with the help of K means clustering. In this technique, we may use various types of attributes like the volume of data consumed, the number of transactions, and even the years of association with a particular brand. Customer segmentation helps us in drafting our advertising and marketing strategies. It also allows us to enhance customer engagement and position a brand among the targeted audience.
Image processing
The technique of K means clustering has been pivotal in image processing as well as segmentation. For instance, we may want to identify various types of objects in a streaming video. For this purpose, we may identify the pixels that have very little difference between them and order them in a common cluster. In this way, we may obtain the K number of clusters by segregating the image in accordance with the pixels. The process of image processing has found applications in examining various types of CCTV footage for security purposes.
Concluding remarks
In the present times, we usually rely on the usage of R for performing k-means clustering. Using R, we are able to analyze and visualize large data sets in accordance with the R plot function. The applications of k-means clustering are slated to witness a huge surge given the analytical processes it becomes relevant to in the near future.
