告诉我一些我不知道的：迭代数据采掘的随机化策略
Tell Me Something I Don't Know: Randomization Strategies for Iterative Data Mining
摘要：
有各种各样的数据采掘方法可用，并且在探索性数据分析中，对同一数据集使用许多不同的方法通常是有用的。然而，这导致了一个问题，即一种方法发现的结果是否反映了另一种方法的结果所显示的现象，或者这些结果是否在某种意义上描述了数据的不相关属性。例如，使用聚类可以给出清晰的聚类结构的指示，并且计算变量之间的相关性可以显示数据中存在许多显著的相关性。然而，可能的情况是，相关性实际上是由集群结构决定的。在本文中，我们考虑的问题，随机化的数据，使以前发现的模式或模型被考虑在内。随机化方法可用于迭代数据采掘。在数据采掘过程中的每一步，随机化从满足已经发现的模式或模型的数据矩阵集合中产生随机样本。也就是说，给定一个数据集和一些统计数据（例如，聚类中心或共现计数），随机化方法对具有与原始数据集相似的给定统计值的数据集进行采样。我们使用基于本地交换的Metropolis抽样来实现这一点。我们描述了真实数据的实验，证明了我们的方法的实用性。我们的研究结果表明，在许多情况下，结果，例如，聚类实际上意味着例如频繁模式发现的结果。
英文摘要：
There is a wide variety of data mining methods available, and it is generally useful in exploratory data analysis to use many different methods for the same dataset. This, however, leads to the problem of whether the results found by one method are a reflection of the phenomenon shown by the results of another method, or whether the results depict in some sense unrelated properties of the data. For example, using clustering can give indication of a clear cluster structure, and computing correlations between variables can show that there are many significant correlations in the data. However, it can be the case that the correlations are actually determined by the cluster structure. In this paper, we consider the problem of randomizing data so that previously discovered patterns or models are taken into account. The randomization methods can be used in iterative data mining. At each step in the data mining process, the randomization produces random samples from the set of data matrices satisfying the already discovered patterns or models. That is, given a data set and some statistics (e.g., cluster centers or co-occurrence counts) of the data, the randomization methods sample data sets having similar values of the given statistics as the original data set. We use Metropolis sampling based on local swaps to achieve this. We describe experiments on real data that demonstrate the usefulness of our approach. Our results indicate that in many cases, the results of, e.g., clustering actually imply the results of, say, frequent pattern discovery.
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。
