Combining granular clustering and classifications in knowledge based networks

在基于知识的网络中结合粒度聚类和分类

• 批准号: 123746-2007
• 负责人: Lingras, Pawan
• 金额: $ 1.02万
• 依托单位: Saint Mary's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=503308
• 关键词: Combining; granular; clustering; classifications; knowledge

Knowledge acquired from databases may be fragmented for two principal reasons: the databases may be physically stored in different locations distributed on a network, or it may be computationally advantageous to apply a data mining technique to subsets of a massive database. In such cases, combining fragmented knowledge is an important issue. Our long term goal is to combine granular clustering and classification results in a knowledge-oriented network. Granular computing typically encompasses algorithms based on fuzzy, rough, and interval sets, which were initially used in classification problems. Recently, researchers have shown that these three representations are useful for modeling overlapping clusters. Fuzzy clustering makes it possible to specify the degree with which a given object belongs to a cluster, instead of a binary description of the membership. The interval and rough set representations allow us to state that an object may belong to more than one cluster. Our proposals that use interval set representations of clusters have been well received by the research community. These approaches were based on Genetic Algorithms, K-means, and Kohonen Self-organizing Maps. We are also collaborating with other researchers on support vector interval set clustering. These interval set representations are more flexible than the conventional crisp clusters and less verbose than the fuzzy clusters. The distributed pieces of databases in a knowledge-oriented network may differ from each other because they contain different features and/or they contain different objects. For our short term goal, we will focus on databases that differ in features used to represent the objects. These features may be semantically different from each other, such as customer spending and customer visits. Or, they may be the same attributes from different time periods, for example, spending in summer versus spending in winter. When two or more significantly different criteria are used in the classification or clustering, it may be desirable to create separate models for each criteria, and then combine the results. We plan to extend this concept by combining conventional and granular classifications and clustering from different time periods.

从数据库获得的知识可能是碎片化的，主要有两个原因：数据库可能物理地存储在网络上分布的不同位置，或者将数据挖掘技术应用于大型数据库的子集可能在计算上有利。在这种情况下，整合碎片化的知识是一个重要的问题。我们的长期目标是在面向知识的网络中结合颗粒聚类和分类结果。颗粒计算通常包括基于模糊集、粗糙集和区间集的算法，这些算法最初用于分类问题。最近，研究人员已经证明这三种表示对于重叠聚类的建模是有用的。模糊聚类可以指定给定对象属于集群的程度，而不是对隶属关系的二元描述。区间和粗糙集表示允许我们声明一个对象可能属于多个集群。我们提出的使用区间集表示聚类的建议得到了研究界的好评。这些方法基于遗传算法、K-means和Kohonen自组织图。我们还与其他研究人员合作研究支持向量区间集聚类。这些区间集表示比传统的清晰聚类更灵活，比模糊聚类更简洁。面向知识的网络中的分布式数据库可能彼此不同，因为它们包含不同的特征和/或它们包含不同的对象。对于我们的短期目标，我们将重点关注用于表示对象的特征不同的数据库。这些功能可能在语义上彼此不同，例如客户支出和客户访问。或者，它们可能是来自不同时间段的相同属性，例如，夏季消费与冬季消费。当在分类或聚类中使用两个或更多明显不同的标准时，可能需要为每个标准创建单独的模型，然后组合结果。我们计划通过结合来自不同时间段的传统和颗粒分类和聚类来扩展这个概念。