I'm trying to classify my set of data into two classes (introvert / extrovert). I was thinking of using a decision tree at first, but I don't have any potential known results in order to create my decision tree model. Thus I decided to use a k-means clustering algorithm with k = 2.
Since the clustering algorithm accepts only numeric values, can I use the decision tree algorithm to transform some type of values I have into numeric ones at first (based on some rules I define within the tree) before I start clustering?
Let's suppose at the end of the algorithm I get my 2 clusters: cluster 1 and cluster 2. How can I classify these two clusters based on my 2 classes? Am I supposed to use supervised or semi-supervised clustering? (I don't know how semi- and supervised clustering work).
Is there any other simple and efficient classification technique that can satisfy my needs?
P.S. I'm new to this domain and all your advice and remarks are appreciated.