Let’s say you work in a financial firm which deals with credit card transactions.There is a lot of data that comes in every second. The only problem is that it is difficult to find humans who will mark each and every transaction either as a valid or genuine transaction or a fraud. When we do not have any information about a transaction being fraud or genuine, the problem becomes an unsupervised problem. To tackle these kinds of problems we have to think about how many clusters candata be divided into. Clustering is one of the approaches that you can use forproblems like this, but it must be noted that there are several other approaches available that can be applied to unsupervised problems. For a fraud detection problem, we can say that data can be divided into two classes (fraud or genuine).假设你在一家从事信用卡交易的金融公司工作，每秒钟都有大量的数据。唯一的问题是，很难找到将每一笔交易标记为有效或真实交易或欺诈的人。当我们没有任何关于欺诈或真实交易的信息时，问题就变成了无监督的问题。为了解决这类问题，我们必须考虑将数据分成多少个簇。聚类是一种可以用于解决此类问题的方法，但必须注意的是，还有其他几种方法可以应用于无监督问题。对于欺诈检测问题，我们可以说数据可以分为两类（欺诈或真实）。