通过有偏对抽样发现关联和计算相似性
Finding Associations and Computing Similarity via Biased Pair Sampling
摘要：
此版本 * 被完整版本取代 *，完整版本可在http：//www.itu.dk/people/pagh/papers/mining-jour.pdf上找到，其中包含更强的理论结果，并修复了实验报告中的一个错误。摘要：以前已经提出了基于采样的方法来解决在数据中发现有趣的关联的问题，即使是低支持度的项目。虽然这些方法不能保证精确的结果，但它们比依赖于精确计数的方法要有效得多。然而，对于许多相似性度量，还没有这样的方法。在本文中，我们展示了如何各种各样的措施可以支持一个简单的有偏抽样方法。该方法还扩展到寻找高置信度的关联规则。我们从理论上证明，我们的方法是优于精确的方法时，“有趣的相似性/置信度”的阈值高于平均成对相似性/置信度，平均支持度不太低。我们的方法在交易包含许多项目时特别好。我们在标准关联挖掘基准测试的实验中证实，这在实际数据集上提供了显着的加速（有时比理论保证大得多）。减少计算时间超过一个数量级，并显着节省空间，观察。
英文摘要：
This version is ***superseded*** by a full version that can be found at http://www.itu.dk/people/pagh/papers/mining-jour.pdf, which contains stronger theoretical results and fixes a mistake in the reporting of experiments. Abstract: Sampling-based methods have previously been proposed for the problem of finding interesting associations in data, even for low-support items. While these methods do not guarantee precise results, they can be vastly more efficient than approaches that rely on exact counting. However, for many similarity measures no such methods have been known. In this paper we show how a wide variety of measures can be supported by a simple biased sampling method. The method also extends to find high-confidence association rules. We demonstrate theoretically that our method is superior to exact methods when the threshold for "interesting similarity/confidence" is above the average pairwise similarity/confidence, and the average support is not too low. Our method is particularly good when transactions contain many items. We confirm in experiments on standard association mining benchmarks that this gives a significant speedup on real data sets (sometimes much larger than the theoretical guarantees). Reductions in computation time of over an order of magnitude, and significant savings in space, are observed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
