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Clarify about BOW Module

I read about the BOW algorithm that can be used in object recognition and classification . I'm working on application to recognize shops and buildings based on Local features matching and geo-location . The data-set consist of 26 POI ,23 of them are shops and three are buildings .

The algorithm say after extracting all the descriptors for each features detected from the data-sets (Train images) to cluster them to a k group/cluster . My first question ,is the number of clusters that i should use equal to my POI in my application which is 26 ? My second question after clustering into K cluster , the algorithm represent each Ki cluster in one center descriptor it's size depends on the Descriptor extractor algorithm used ,for example in SURF 64 real , so how is that one center descriptor can be used in matching, and does the rest descriptors that belong to that center Ki descriptor related to each other in some data structure like tree , because that make sense for me .

I'm sorry if my question not clear because I'm confused with that topic . I will appreciate any help .