I have been looking for an answer/guidance/pointer to this question of mine for a while. After going through many (100s actually) posts and articles, I finally found this question, where this response is what I believe is sending me in the right direction. However, I think I need a little more help to move forward. In the response, Anony-Mousse mentioned the following:
Pixels of an image in RGB space. Least-squares makes some sense and all attributes are comparable - k-means is a good choice.
Geographic data: least-squares is not very appropriate. there will be outliers. but distance is very meaningful. Use DBSCAN if you have a lot of noise, or HAC (hierarchical agglomerative clustering) if you have very clean data.
Species observed in different habitats. Least-squares is dubious, but e. g. Jaccard similarity is meaningful. You probably have only few observations and no "false" habitats - use HAC.
He/She says "Least-square makes sense or least-square is not very appropriate" but does not mention why. Are there any books/concepts that will help me learn the process of choosing a distance metric/clustering technique and explain what characteristics of any given dataset drives distance/technique selection process.
I am currently working on creating customer segmentation on this retail dataset and I just cannot tell which distance metric/clustering I need to choose of 10+ clustering models available for me to implement.