How to classify features into two classes without labels? I have a big dataset with nearly 200 features. However, I do not have class labels for these data. I want to divide these data into two classes based on these features. I know, when we do not have class labels we have to use some clustering method. However, since I do not have any labels, I am just wondering how to measure the accuracy of the models.

Please let me know the most suitable approach that I should follow?

I am happy to provide more details about my featureset if needed :)
As you know, clustering is Unsupervised learning algorithm. Since you don't know the number of clusters, it becomes hard to find the best possible separation (number of clusters). There is a very good paper published on validating clustering techniques. There are 3 criteria defined in this paper for validating your clusters. You can take a look at that.

Also, the link below gives you some code in R for cluster validation. If you want you can try this too-

http://www.sthda.com/english/articles/29-cluster-validation-essentials/97-cluster-validation-statistics-must-know-methods/