III-COR-Small: Multi-Relational Data Clustering with Probabilistic Mixture Models

III-COR-Small：具有概率混合模型的多关系数据聚类

• 批准号: 0812183
• 负责人: Arindam Banerjee
• 金额: $ 39.96万
• 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: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812183&HistoricalAwards=false
• 关键词: III; COR; Small; Multi; Relational

With widespread attempts to apply data mining methods to real lifeproblems, there is an increasing realization that real life data isoften multi-relational, involving observations connecting multipleentities through a set of relations. A central problem in severalapplications involving multi-relational data is to simultaneously findclusters of objects across related entities, e.g., customer clustersand related product clusters in e-commerce, movie clusters and relateduser clusters in recommendation systems, communities and sharedcontent in social networks, etc. The key novel aspect is that theclustering of objects in an entity, such as the set of movies orusers, depends on its relationships with objects in other entities,e.g., users are similar if they like similar movies, and vice versa.The primary goal of of this project is to develop a unifiedstatistical approach to multi-relational clustering and relatedproblems in multi-relational data analysis. Towards this end, theproject investigates a family of novel statistical multi-relationalmixture models, with focus on additive and multiplicative models formulti-relational clustering. Due to modularity of design, bothadditive and multiplicative models can incorporate domain specificsemantics as well as automatic model selection using appropriateBayesian priors. Further, the project investigates efficientvariational inference methods appropriate for discovering latentmulti-relational clusters.The project significantly empowers the knowledge discovery componentof data mining. Crucial clues to understanding observed data inseveral disciplines, including social, biological, and informationsciences, are often spread across multiple related observations. Theproject enables a statistical approach to detecting latent structurein such multi-relational data, which is an important step towardsknowledge discovery from multiple related data sources. The projectplays an important role in developing closer collaboration acrossdisciplines and broaden participation in computer science. Buildingon the increasing awareness regarding the ubiquity of multi-relationaldata, the project contributes to the development of appropriateeducational material for the next generation work-force. Furtherinformation on the project may be found at the project web site:http://www.cs.umn.edu/~banerjee/multi-relational.

随着数据挖掘方法在真实的生活问题中的广泛应用，人们越来越认识到真实的生活数据往往是多关系的，涉及通过一组关系连接多个实体的观察。在涉及多关系数据的几个应用中，一个中心问题是同时发现跨相关实体的对象集群，例如，电子商务中的客户集群和相关产品集群、推荐系统中的电影集群和相关用户集群、社交网络中的社区和共享内容等。关键的新颖方面在于，实体中的对象（诸如电影或用户的集合）的集群取决于其与其他实体中的对象的关系，这个项目的主要目标是开发一个统一的统计方法来解决多关系数据分析中的多关系聚类和相关问题。 为此，该项目研究了一系列新的统计多关系混合模型，重点是加法和乘法模型公式关系聚类。由于模块化的设计，无论是加性和乘性模型可以纳入域特定的语义，以及自动模型选择使用适当的贝叶斯先验。 此外，该项目研究了有效的变分推理方法，适用于发现潜在的多关系集群。该项目显着授权的知识发现组件的数据挖掘。理解社会学、生物学和信息科学等多个学科中观测数据的关键线索通常分布在多个相关观测中。该项目使一种统计方法，以检测潜在的结构，在这样的多关系数据，这是一个重要的一步，以知识发现从多个相关的数据源。该项目在发展跨学科更密切的合作和扩大计算机科学的参与方面发挥了重要作用。 基于对多关系数据普遍性的日益认识，该项目有助于为下一代劳动力开发适当的教育材料。关于该项目的进一步信息可在项目网站http://www.cs.umn.edu/~banerjee/multi-relational上查阅。