K-means clustering is a type of unsupervised machine learning algorithm. Unlike supervised learning (like regression or classification) where you have a clear target to predict, in unsupervised learning you're trying to find interesting patterns or structure in the data.

The dataset we're working with is an Online Retail Data Set from UCI Machine Learning Repository, which contains various transactions. Each transaction has features such as a unique identifier (InvoiceNo), a description (Description), the quantity (Quantity), when it was invoiced (InvoiceDate), the unit price (UnitPrice), the customer identifier (CustomerID), and the country where the transaction was made (Country).

So we've got the data for a bunch of customers, and we want to group them into k clusters based on their behaviors, which can be represented by 'Freq' (frequency of purchases), 'SaleAmount' (total amount spent), and 'ElapsedDays' (the days since their last purchase). The goal of the exercise is to find groups or clusters of customers based on these shopping behaviors. Once we have these clusters, we can analyze them to understand the common characteristics of the customers in each cluster.

Clustering customers like this can be useful for a variety of reasons. For example, the business could target customers with specific marketing campaigns based on their cluster, or use this information to provide personalized recommendations. They could also use this information to identify loyal customers, or those who might be at risk of churning.

To sum up, K-means clustering is a type of unsupervised learning, which is used when we have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K.