I have a dataset that has 5 variables (columns). These are median house price, median income, number of people with no educational degree, number of people with high school degree, number of people with university degree. I have these variables by different census sub-divisions. So rows represent different census sub-divisions. I am interested to segment this data into smaller number of segments e.g. (Low, Average, High) income, (Low, Average, High) house, (Highly or Less) educated.
In other words, I want to e.g. find a way to define ranges for (Low, Average, High) income levels in following two cases:
- By considering the geographical location i.e. census sub-divisions
- Without considering the geographical location i.e. census sub-divisions.
At first I thought that maybe k-means clustering is appropriate (at least for the 2nd case above where I am not considering the census sub-divisions). But this does not seem right to me as I am having both prices/incomes and number of people in the dataset. Also this approach gives me centers of each cluster and not actually the ranges for each levels: Low, Average, and High.
Is there any other approach I can use?