A clustering framework for the process of knowledge discovery in databases

数据库中知识发现过程的聚类框架

• 批准号: 250960-2006
• 负责人: Ester, Martin
• 金额: $ 2.23万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=394834
• 关键词: clustering; framework; process; knowledge; discovery

Knowledge Discovery in Databases (KDD) has been defined as the process of extracting valid, novel, understandable and potentially useful patterns from large databases. The KDD process involves several steps, in particular focusing, pre-processing, data mining and evaluation, that normally have to be iterated to achieve satisfactory results. So far, most KDD research has focused on the data mining step, developing efficient algorithms for tasks such as clustering, classification and association rule mining. Unfortunately, not much research has addressed the other steps and the process as a whole, which has seriously limited the usefulness of existing data mining methods. In this project, we want to explore support for the entire KDD process in the context of clustering, one of the most important data mining tasks. The lack of support for all KDD steps is especially problematic for clustering due to its unsupervised, exploratory nature and because most clustering algorithms do not generate explicit patterns, but return clusters simply as sets of objects. The objective of this proposed project is to develop a framework for clustering supporting all KDD steps. Most existing clustering algorithms exploit only attributes of the objects to be clustered, but in many emerging applications relationships among the target table and attributes from related tables play an important role in representing the objects of interest. In market segmentation, e.g., not only the purchasing preferences but also the social network among the customers is relevant for clustering. When clustering gene expression data, as another example, attributes of the related proteins and their further relationships must be considered. We plan to evaluate our clustering framework in close collaboration with domain experts in the applications of analysis of gene expression data, analysis of flow cytometry data as well as community identification and market segmentation.

数据库中的知识发现（KDD）被定义为从大型数据库中提取有效的、新颖的、可理解的和潜在有用的模式的过程。 KDD 过程涉及几个步骤，特别是聚焦、预处理、数据挖掘和评估，通常必须迭代才能获得满意的结果。到目前为止，大多数 KDD 研究都集中在数据挖掘步骤，为聚类、分类和关联规则挖掘等任务开发有效的算法。不幸的是，没有太多研究涉及其他步骤和整个过程，这严重限制了现有数据挖掘方法的实用性。在这个项目中，我们希望在集群（最重要的数据挖掘任务之一）的背景下探索对整个 KDD 流程的支持。缺乏对所有 KDD 步骤的支持对于聚类来说尤其成问题，因为它具有无监督、探索性的性质，而且大多数聚类算法不会生成显式模式，而是简单地将聚类作为对象集返回。该拟议项目的目标是开发一个支持所有 KDD 步骤的集群框架。大多数现有的聚类算法仅利用要聚类的对象的属性，但在许多新兴应用中，目标表和相关表的属性之间的关系在表示感兴趣的对象方面发挥着重要作用。例如，在市场细分中，不仅购买偏好而且客户之间的社交网络都与聚类相关。作为另一个例子，当对基因表达数据进行聚类时，必须考虑相关蛋白质的属性及其进一步的关系。我们计划与基因表达数据分析、流式细胞术数据分析以及社区识别和市场细分应用领域的专家密切合作，评估我们的聚类框架。