I segmented products by using k-means clustering into 10 clusters with historic data (dispatch data). For new products, I can use some dimension and feature based data (ex: product size, color, budget v.s.). My purpose is matching new products with already existing clusters.
Is this possible?
Thank you for your answers.
Actually I used k-medoids for clustering my data.
As a second step, from your suggests, the best way is, using these clusters as target for classification make sense to me.
But; I have new troubles according to usage of classifiers:
- Recursive Partitioning and Regression Trees: Only used one feature. Message: Variables actually used in tree construction:  ProductCode
- Random Forest: Error Message: Can not handle categorical predictors with more than 53 categories.
- KNN: Data must be scaled before usage, but my data has lots of categorical features. So KNN is not suitable for my problem.
- SVM: I think this classifier wants only 2 features. Error Mesage: contrasts can be applied only to factors with 2 or more levels
So, what classifier should I use?