A clustering and classification question 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.
 A: Don't abuse clustering for classification.
How do you plan on directing the algorithm to not produce two clusters that correspond to


*

*Males vs. Females?

*Blondes vs. Brunettes (with red haired noise)

*Short vs. Tall people?

*People whose first name has even length vs. odd length?


All of these would be meaningful clusters, wouldn't they?
If you have a particular objective to solve - in your case, introverts vs. extroverts - you need to direct your algorithm accordingly. Algorithms cannot do magic, they need direction. So most likely you need training data.
A: If you are indeed researching with interest to the effect of extraversion, you will mostly likely use a reliable and validated questionaire, which results in an extraversion score. 
If you did not do so, and you now want to seperate a dataset (which you did not further describe) into two categories, you can do so. 
There are plenty of algorithms (k-means, EM for GMM, DBSCAN,...) which will cluster your data. Yet the clustering is based on some underlying assumption about the nature of data and the definition of a cluster. For example most algorithms will consider all features equally relevant. 
Yet resulting groups are most probably not related - at all - to the psychological construct of extraversion. (Examples of possible clustering results not related to extraversion in the answer of @Anony-Mousse)
If you want to group (=cluster) people regarding their extraversion, you will have to start from a theoretical perspective and think about which features capture behaviour related/influenced by extraversion and which do not. You are effectively constructing a questionaire doing so and you will have to think about both reliability and validity. 
Or you will just use a validated instrument such as the NEO-PI-R (as linked in the first sentence). 
