If I have collected data regarding say food-preferences from multiple sources and merged them.
How can I decide what kind of clustering to do if I want to find related preferences?
Whether to go for K means, hierarchical, density-based, etc. ?
Is there any process of selecting the clustering technique?
In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means labelled clustering is all about. The term K is basically is a number and you need to tell the system how many clusters you need to perform. If K is equal to 2, there will be 2 clusters if K is equal to 3, 3 clusters and so on and so forth. That's what the K stands for and of course, there is a way of finding out what is the best or optimum value of K.
https://www.google.com/url?sa=t&source=web&rct=j&url=https://medium.com/predict/three-popular-clustering-methods-and-when-to-use-each-4227c80ba2b6&ved=2ahUKEwi7mM785p7sAhXxjOYKHUBmB6EQFjAKegQIARAB&usg=AOvVaw1_6WwpzfOEh3uItj96PO63
Please refer the above link . You can get some basic idea from that. I hope it helps
If you know (or any rough idea) how many clusters you need, then you may try K-means or some other variants of it like K-means++ or MinMax K-means clustering algorithm. But if you do not have prior knowledge about number of clusters then you can try with different values of k, and assess the goodness of the results using some cluster validity indices like DB index, C index, CH index, Dunn index etc. Otherwise you can DBSCAN clustering algorithm.
Ram Sarkar Is the use of DBSCAN suggested for its ability to filter noise as K-means and Hierarchical Clustering are affected by outliers.
Ashay Singh I guess DBSCAN is mostly used when there is no prior knowledge about the number of probable clusters present in the dataset. It automatically selects the number of clusters, whereas in case of K-means or its successors it is required to set a value for k.
Ram Sarkar In DBSCAN it is not required to know the number of clusters prior to code. It automatically clusters the nearest points based on the radius distance of points and the number of points that should be present in the neighborhood.
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