Similarity join is a key operation in metric databases. It retrieves all pairs of elements that are similar. Solving such a problem usually requires comparing every pair of objects of the datasets, even when indexing and ad hoc algorithms are used. We propose a simple and efficient algorithm for the computation of the approximated $k$ nearest neighbor self-similarity join. This algorithm computes $\Theta \left(n^{3∕2}\right)$ distances and it is empirically shown that it reaches an empirical precision of 46% in real-world datasets. We provide a comparison to other common techniques such as Quickjoin and Locality-Sensitive Hashing and argue that our proposal has a better execution time and average precision.

相似性联接是度量标准数据库中的关键操作。它检索所有相似的元素对。解决此问题通常需要比较数据集的每对对象，即使使用索引和即席算法也是如此。我们提出了一种简单有效的算法来计算近似值$ķ$最近邻居自相似联接。该算法计算$\Theta （{ñ}^{3∕2}）$距离，并根据经验表明，它在实际数据集中达到46％的经验精度。我们提供了与其他常见技术（如快速联接和局部敏感哈希）的比较，并认为我们的建议具有更好的执行时间和平均精度。