How to determine x and y in 2 dimensional K-means clustering? Recently, we were taught K-means clustering. I understood the basic idea of the algorithm and successfully implemented it for data with a single dimensional. Now we are told to implement it for two dimensional data. As far as I understood x and y can be two attributes of a dataset but our professor said otherwise. She said that the we have to determine x and y of an attribute in the data set to cluster the data. She used a simple 2D matrix as an example. This has got me confused. How can one determine x and y of an atttribute? Row number and column number seems silly to me to use it for distance calculation.

So, my question is how does one determine x and y for 2-D k means clustering?

As per this question the two attributes (weight and height) are used as x and y. Is this correct?
I see no problem with the example of clustering on two numerical attributes like height and weight like in the example.

The only thing I can think of is somewhere lost in translation, your professor was trying to explain the concept of reducing many dimensions (attributes) to two and then clustering on those derived dimensions. That is a common technique for trying to visually spot clusters in a complex data set.