Quantile-Based KNN Over Multi-Valued Objects

K Nearest Neighbor search has many applications including data mining, multi-media, image processing, and monitoring moving objects. In this paper, we study the problem of KNN over multi-valued objects. We aim to provide effective and efficient techniques to identify KNN sensitive to relative distributions of objects. We propose to use quantiles to summarize relative-distribution-sensitive K nearest neighbors. Given a query Q and a quantile (0, 1], we firstly study the problem of efficiently computing K nearest objects based on a -quantile distance (e.g. median distance) from each object to Q. The second problem is to retrieve the K nearest objects to Q based on overall distances in the "best population" (with a given size specified by -quantile) for each object. While the first problem can be solved in polynomial time, we show that the 2nd problem is NP-hard. A set of efficient, novel algorithms have been proposed to give an exact solution for the first problem and an appr...

• Type: Conference
• Year: 2010
• Where: ICDE
• Authors: Wenjie Zhang, Xuemin Lin, Muhammad Aamir Cheema, Ying Zhang, Wei Wang 0011
• Keywords: Database, Efficient Techniques, ICDE 2010, Monitoring Moving Objects, Multi-valued Objects