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Exclusive condition on item partition in fuzzy co-clustering based on K-L information regularization

机译:基于K-L信息正规化的模糊共聚物中项目分区的独占条件

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FCCM based on K-L information regularization is an FCM-type co-clustering model, which is a fuzzy counterpart of the probabilistic Multinomial Mixture Models (MMMs). In MMMs and other FCM-type co-clustering models, whose goal is to simultaneously partition objects and items considering their mutual cooccurrence information, memberships of objects are forced to be exclusive in a similar way to FCM while item-memberships only represent the relative typicality in each cluster and are not forced to be exclusive. In this paper, a new co-clustering model is proposed by introducing the penalty for avoiding cluster overlapping in sequential fuzzy cluster extraction, which brings exclusive partition of items.
机译:基于K-L信息正则化的FCCM是FCM型共聚类模型,是概率多项混合模型(MMMS)的模糊对应物。 在MMMS和其他FCM型共簇模型中,其目标是同时分区对象和项目考虑其相互协商的信息,对象的成员资格被迫以类似的方式排斥,而项目成员资格仅代表相对典型程度 在每个集群中,并不被迫独家。 在本文中,提出了一种新的共聚类模型,提出了避免在顺序模糊簇提取中的群体重叠的惩罚,这带来了物品的独家分区。

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