Unsupervised Machine Learning: Data Patterns and Clustering Insights

Unsupervised Machine Learning focuses primarily on input vectors that correspond to target values, which is essential in interpreting information based on similarities, patterns, and differences. Therefore, unsupervised machine learning involves using a concise representation of data to generate imaginative content from the data. K-means clustering is an example of unsupervised machine learning that partitions existing datasets into given clusters. This algorithm assigns each data component to one of the existing K clusters, which is similar (Sinaga & Yang, 2020). Clustering aims to reduce the variance between each existing cluster by maximizing the variance between other clusters. K-means Clustering works where the algorithm is initialed by selecting. Then, each data is assigned to a closer centroid using Euclidean distance to form K clusters. The centroids are then updated by recalculating the means of the data points on each cluster. Then, assignment and update processes are repeated to enable the centroids to change and indicate their convergence. 

A practical application example of unsupervised machine learning of K-means Clustering in its usage in the retail industry. Most businesses always use K-Means Clustering while segmenting their customers. This involves analysis of customer purchase data, where customers can be grouped into relevant categories based on their purchase behavior. An example is when a given retailer has a dataset that includes customers' purchase history, the frequency of their purchases, and the expenditure of the products purchased (Sinaga & Yang, 2020). Through the application of K-Means Clustering, the retailer is in the position to identify different customer segments. These segments include frequent shoppers, which covers the customers who frequently visit and make purchases—big spenders who spend high amounts. Bargain hunters are customers who purchase items on promotion or discounted items—occasional shoppers characterized by making small purchases and irregular visits. When a retailer understands these segments, the individual is leveraged to tailor the desired marketing strategies, customize promotions, and achieve customer satisfaction by targeting a given market and the nature of services. A perfect example is giving elusive deals to big spenders and also rewarding customers who shop regularly. 

Reference

Sinaga, K. P., & Yang, M. S. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8, 80716-80727. https://iiast.iaic-publisher.org/ijcitsm/index.php/IJCITSM/article/view/122