In this paper, we present our research on similarity search problems. Similarity search problems define the distances between data points and a given query point Q, efficiently and effectively selecting data points which are closest to Q. It can be applied in various data mining fields. In many applications, information similar to multiple queries is required. In this paper, we explore the meaning of K nearest neighbors from a new perspective, define the distance between a data point and a query point set, and propose an algorithm to find nearest neighbors to multiple queries with possibly different degrees of importance. Our approach works for both full similarities and partial similarities in subsets of dimensions.
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