As data volumes continue to grow, clustering and outlier detection algorithms are becoming increasingly time-consuming. Classical index structures for neighbor search are no longer sustainable due to the "curse of dimensionality". Instead, approximated index structures offer a good opportunity to significantly accelerate the neighbor search for clustering and outlier detection and to have the lowest possible error rate in the results of the algorithms. Locality-sensitive hashing is one of those. We indicate directions to model the properties of LSH.

中文翻译：

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建立LSH模型

随着数据量的不断增长，聚类和离群值检测算法变得越来越耗时。由于“维数的诅咒”，用于邻居搜索的经典索引结构不再可持续。取而代之的是，近似的索引结构提供了一个很好的机会，可以极大地加速邻居搜索以进行聚类和离群值检测，并在算法结果中具有最低的错误率。局部敏感的哈希就是其中之一。我们指出了对LSH属性进行建模的方向。