III: Small: Generalization of the Association Analysis Framework

关联分析找到描述用于表征一组对象的二进制属性(变量)之间的关系的模式。关联模式挖掘的一个关键优点是，通过使用基于支持的模式剪枝，即消除由太少事务支持的模式，通常可以使搜索空间的潜在指数性质变得容易处理。尽管关联挖掘的理论基础已经很成熟，但在许多科学领域中，这组技术并没有被广泛用作数据分析工具。例如，在生物信息学和计算生物学领域，虽然使用聚类和分类技术很常见，但很少使用来自关联分析的技术。这是因为传统的关联分析框架及其当前的扩展不能有效地捕获生物信息学和其他领域中所需的许多模式。虽然这种模式可以通过诸如双向聚类和共同聚类之类的技术来发现，但是这些方法受到一些严重限制，最显著的是，在不求助于危及搜索完整性的启发式方法的情况下，无法有效地探索搜索空间。为了应对上述挑战，该小组将扩展传统的关联分析框架。他们提出了两种新的直接从实值数据中挖掘模式的框架，与双聚类和联合聚类不同，它们能够发现满足给定约束的所有模式，并且不受离散化和其他数据转换方法造成的信息损失的影响。他们还将扩展基于关联分析的方法来处理具有类别标签的数据，方法是有效地使用可用的类别标签信息来修剪指数搜索空间，并找到区分两个数据类别的低支持度模式。为了评价这项工作的结果，他们将制定强有力的评价方法，以评价从拟议框架中获得的模式。这项拟议的工作有望将关联分析的能力扩展到健康和生命科学中的广泛新应用，例如从单核苷酸多态和基因表达数据中发现生物标记物和功能模块，在个性化医学以及药物和生物燃料的开发中具有潜在的应用。

Association analysis finds patterns that describe the relationships among the binary attributes (variables) used to characterize a set of objects. A key strength of association pattern mining is that the potentially exponential nature of the search space can often be made tractable by using support based pruning of patterns i.e., eliminating patterns supported by too few transactions. Despite the well-developed theoretical foundation of association mining, this group of techniques is not widely used as a data analysis tool in many scientific domains. For example, in the domain of bioinformatics and computational biology, while the use of clustering and classification techniques is common, techniques from association analysis are rarely employed. This is because many of the patterns required in bioinformatics and other domains are not effectively captured by the traditional association analysis framework and its current extensions. Although such patterns can be found by techniques such as bi-clustering and co-clustering, these approaches suffer from a number of serious limitations, most notably, an inability to efficiently explore the search space without resorting to heuristic approaches that compromise the completeness of the search. To address the challenges mentioned above, the team will extend the traditional association analysis framework. They propose two novel frameworks for directly mining patterns from real-valued data that, unlike biclustering and co-clustering, are able to discover all patterns satisfying the given constraints and do not suffer from the loss of information caused by discretization and other data transformation approaches. They will also extend association analysis based approaches to work with data that has class labels by effectively using the available class label information for pruning the exponential search space and finding low-support patterns that discriminate between the two data classes. To evaluate the results of the work, they will develop robust evaluation methodologies for evaluating the patterns obtained from the proposed frameworks. The proposed work promises to extend the power of association analysis to a wide range of new applications in health and life sciences, such as the discovery of biomarkers and functional modules from single nucleotide polymorphism and gene expression data, with potential applications in personalized medicine and the development of drugs and bio-fuels.