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Fuzzy clustering approach for star-structured multi-type relational data

机译:星型多类型关系数据的模糊聚类方法

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Recently, mining interrelated data among multiple types of objects attracts a lot of attention due to its importance in many real-world applications. Despite of extensive study on fuzzy clustering of vector space data and homogeneous relational data, very limited exploration has been made on fuzzy clustering of relational data involving several object types. In this paper, we propose FC-SMR, a fuzzy approach for clustering star-structured multi-type relational data, where the central type is related to multiple attribute types. In FC-SMR, objects of the central type are clustered based on the rankings of objects of different attribute types. We formulate the clustering problem as a constrained maximization problem and give an efficient algorithm for finding local solutions of the defined objective function. Experimental studies conducted on real-world document data show the effectiveness of the new approach.
机译:最近,由于在许多实际应用中的重要性,在多种类型的对象之间挖掘相互关联的数据引起了很多关注。尽管对向量空间数据和齐次关系数据的模糊聚类进行了广泛的研究,但是对涉及几种对象类型的关系数据的模糊聚类进行的探索非常有限。在本文中,我们提出FC-SMR,这是一种将星型多类型关系数据聚类的模糊方法,其中中心类型与多种属性类型有关。在FC-SMR中,根据不同属性类型的对象的排名对中心类型的对象进行聚类。我们将聚类问题公式化为约束最大化问题,并给出了一种有效的算法,用于找到定义的目标函数的局部解。对真实文档数据进行的实验研究表明了这种新方法的有效性。

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