Top-k query processing for combinatorial objects using Euclidean distance

Conventional search techniques are mainly designed to return a ranked list of single objects that are relevant to a given query. However, they do not meet the criteria for retrieving a combination of objects that is close to the query. This paper presents top-k query processing in which Euclidean distance is used as the scoring function for combinatorial objects. We also propose a pruning method based on clustering and efficiently select object combinations by pruning clusters that do not contain potential candidates for the top-k results. We compared the proposed method with the method that enumerates all the combinatorial objects and calculates the distance to the query. Experimental results revealed that the proposed method improves the processing efficiency to about 95% at maximum.