近年來，反向K最近臨點(RkNN)的問題受到了合理的觀注，因為它可以被用在商業地點的選擇、以profile為基礎的行銷、或者資料探勘的用途上。然而，先前在度量空間中的RkNN演算法都不能允許使用者在每次查詢時使用不同的距離函式。在這篇論文中，我們定義了可調整距離函式之RkNN問題，並且提供一個完整的解決方案。

In recent years, the reverse k-nearest neighbor (RkNN) problem in metric spaces
has attracted reasonable attention because it can be applied to business location
planning, profile-based marketing, clustering and outlier detection. However, previous
works on the RkNN problem in metric spaces cannot allow users to assign different
distance functions for distinct queries. In this thesis, we define the problem on RkNN
query with adjustable functions. That is, for each distinct query, it can have its own
distance function. To the best of our knowledge, this thesis is the first work solving
the problem of RkNN with adjustable distances in metric spaces. We propose a
generic framework to handle the RkNN queries with different distance functions. In
our framework, a new index structure is built by using multiple distance functions.
Therefore, some pruning rules determined by calculating the correlation between the
distance function assigned by the user and those used to build the index structure can
be provided to prune the irrelevant data points and then, the remainder candidates are
really checked to see whether they are real results. The correctness of our pruning
strategies is proved and the experiment results demonstrate the efficiency and
effectiveness of our approach.

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