Basic Consideration of Co-Clustering Based on Rough Set Theory
Basic Consideration of Co-Clustering Based on Rough Set Theory
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DOI：
10.1007/978-3-030-62509-2_13
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发表时间：
2020-11
期刊：
影响因子：
--
通讯作者：
S. Ubukata;Narihira Nodake;A. Notsu;Katsuhiro Honda
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文献类型：
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作者：
S. Ubukata;Narihira Nodake;A. Notsu;Katsuhiro Honda
在聚类领域，粗糙聚类是一种基于粗糙集理论的聚类，是一种很有前途的处理对象所属簇的确定性、可能性和不确定性的方法。广义粗糙C-均值(GRCM)是硬C-均值(HCM；k-Means)的一种基于粗糙集的扩展，它通过将对象分配到其相对较近的簇的上部区域来提取重叠的簇结构。共聚类是一种有用的技术，用于总结对象和项目之间的共现信息，例如文档中关键字的频率和用户的购买历史。基于多项式混合模型的模糊共聚类(FCCMM)是一种基于统计模型的共聚类方法，它引入了一种调整对象和项目模糊度的机制。在借鉴GRCM和FCCMM的基础上，提出了一种新的粗糙共聚类方法--基于多项混合模型的粗糙共聚类(RCCMM)。RCCMM的目标是通过考虑确定性、可能性和不确定性，适当地提取共现信息中固有的重叠共聚结构。通过数值实验，验证了该方法是否能够正确提取重叠的共簇结构。
In the field of clustering, rough clustering, which is clustering based on rough set theory, is a promising approach for dealing with the certainty, possibility, and uncertainty of belonging of object to clusters. Generalized rough C-means (GRCM), which is a rough set-based extension of hard C-means (HCM; k-means), can extract the overlapped cluster structure by assigning objects to the upper areas of their relatively near clusters. Co-clustering is a useful technique for summarizing co-occurrence information between objects and items such as the frequency of keywords in documents and the purchase history of users. Fuzzy co-clustering induced by multinomial mixture models (FCCMM) is a statistical model-based co-clustering method and introduces a mechanism for adjusting the fuzziness degrees of both objects and items. In this paper, we propose a novel rough co-clustering method, rough co-clustering induced by multinomial mixture models (RCCMM), with reference to GRCM and FCCMM. RCCMM aims to appropriately extract the overlapped co-cluster structure inherent in co-occurrence information by considering the certainty, possibility, and uncertainty. Through numerical experiments, we verified whether the proposed method can appropriately extract the overlapped co-cluster structure.