Providing Diversity in K-Nearest Neighbor Query Results

Abstract. Given a point query Q in multi-dimensional space, K-Nearest Neighbor (KNN) queries return the K closest answers in the database with respect to Q. In this scenario, it is possible that a majority of the answers may be very similar to one or more of the other answers, especially when the data has clusters. For a variety of applications, such homogeneous result sets may not add value to the user. In this paper, we consider the problem of providing diversity in the results of KNN queries, that is, to produce the closest result set such that each answer is sufficiently different from the rest. We first propose a user-tunable definition of diversity, and then present an algorithm, called MOTLEY, for producing a diverse result set as per this definition. Through a detailed experimental evaluation, we show that MOTLEY can produce diverse result sets by reading only a small fraction of the tuples in the database. Further, it imposes no additional overhead on the evaluation of tra...

• Type: Conference
• Year: 2004
• Where: PAKDD
• Authors: Anoop Jain, Parag Sarda, Jayant R. Haritsa
• Keywords: Data Mining, K-nearest Neighbor, KNN Queries, PAKDD 2004, Traditional Knn Queries