子采样下的鲁棒项集
Finding Robust Itemsets Under Subsampling
摘要：
模式爆炸问题一直困扰着频繁模式的挖掘，使得模式约简技术成为模式挖掘中的一个关键挑战。在本文中，我们提出了一个新的理论框架模式减少。我们通过测量项集的属性（如封闭性或不可导性）的鲁棒性来做到这一点。属性的鲁棒性是该属性在原始数据的随机子集上保持的概率。我们研究四个属性：如果一个项集是封闭的，自由的，不可导的或完全破碎的，并演示如何计算的鲁棒性分析，而无需实际采样的数据。我们的鲁棒性概念有很多优点：与减少模式的统计方法不同，我们不假设零假设或任何噪声模型，与噪声容忍或近似模式相反，给定属性的鲁棒模式总是具有此属性的模式的子集。如果底层属性是单调的，那么度量也是单调的，允许我们有效地挖掘健壮的项集。我们进一步推导出一个无参数的技术，可用于顶级$k$方法的项目集的排名。我们的实验表明，我们可以成功地使用鲁棒性措施，以减少模式的数量和排名产生有趣的项目集。
英文摘要：
Mining frequent patterns is plagued by the problem of pattern explosion making pattern reduction techniques a key challenge in pattern mining. In this paper we propose a novel theoretical framework for pattern reduction. We do this by measuring the robustness of a property of an itemset such as closedness or non-derivability. The robustness of a property is the probability that this property holds on random subsets of the original data. We study four properties: if an itemset is closed, free, non-derivable or totally shattered, and demonstrate how to compute the robustness analytically without actually sampling the data. Our concept of robustness has many advantages: Unlike statistical approaches for reducing patterns, we do not assume a null hypothesis or any noise model and in contrast to noise tolerant or approximate patterns, the robust patterns for a given property are always a subset of the patterns with this property. If the underlying property is monotonic, then the measure is also monotonic, allowing us to efficiently mine robust itemsets. We further derive a parameter-free technique for ranking itemsets that can be used for top-$k$ approaches. Our experiments demonstrate that we can successfully use the robustness measure to reduce the number of patterns and that ranking yields interesting itemsets.
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。
