III: Small: Generalization of the Association Analysis Framework

三：小：关联分析框架的泛化

• 批准号: 0916439
• 负责人: Vipin Kumar
• 金额: $ 50万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2012-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916439&HistoricalAwards=false
• 关键词: III; Small; Generalization; Association; Analysis

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.

关联分析发现描述用于表征一组对象的二进制属性（变量）之间关系的模式。关联模式挖掘的一个关键优势是，通过使用基于支持度的模式修剪，通常可以使搜索空间的潜在指数性质变得易于处理，即，消除了由太少的事务支持的模式。尽管关联挖掘的理论基础已经非常成熟，但这组技术在许多科学领域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，虽然聚类和分类技术的使用很常见，但很少采用关联分析技术。这是因为生物信息学和其他领域所需的许多模式不能被传统的关联分析框架及其当前的扩展有效地捕获。虽然这样的模式可以找到的技术，如双聚类和共聚类，这些方法遭受了一些严重的限制，最值得注意的是，不能有效地探索搜索空间，而不诉诸启发式的方法，妥协的完整性搜索。为了应对上述挑战，该团队将扩展传统的关联分析框架。他们提出了两种新的框架，用于直接从实值数据中挖掘模式，与双聚类和共聚类不同，它们能够发现满足给定约束的所有模式，并且不会受到离散化和其他数据转换方法造成的信息丢失的影响。他们还将扩展基于关联分析的方法，通过有效地使用可用的类标签信息来修剪指数搜索空间并找到区分两个数据类的低支持模式，来处理具有类标签的数据。为了评价工作成果，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。拟议的工作有望将关联分析的力量扩展到健康和生命科学的广泛新应用，例如从单核苷酸多态性和基因表达数据中发现生物标志物和功能模块，并在个性化医疗和药物和生物燃料的开发中具有潜在的应用。