This question is from a confused novice.
I have a data set with where each point is located in a 2-D space defined by two objectives (say, X and Y). I wish to identify a set of points from this space such that their distance from X AND y is minimum.
What can be the most efficient / simple / targeted approach to achieve this ? I started with k-means clustering but eventually ventured into material related to multiobjective optimization, multiobjective clustering, clustering ensemble, etc.
K-means CA will try to identify clusters via unspecified leaning of the data based on internal criteria (compactness and separation), but is there a way to identify clusters using external criteria such as distance of a cluster member from the above X and Y axes?
Will the multiobjective methods be an overkill ? Are the advanced methods unobtrusive? Can these methods work with the dataset that already exists ?
A lead in the right direction will be greatly appreciated. Thank you.