On the Nature of Degree of Indiscerniblity for Rough Clustering
On the Nature of Degree of Indiscerniblity for Rough Clustering
复制标题
论粗聚类不可分辨度的本质
DOI：
10.1109/icsmc.2006.384652
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
S. Tsumoto
中科院分区：
文献类型：
--
作者：
S. Hirano;S. Tsumoto
在本文中，我们研究了不可分辨程度的性质，它是粗糙聚类中的一个主要参数。两个对象的不可分辨程度表示将它们归入同一范畴的等价关系的比率。由于它反映了区分对象的全局共性，所以它可以与分类知识的粗糙性相关联。粗糙聚类在初始等价关系的迭代求精过程中利用不可分辨程度来控制结果簇的粗糙度。然而，由于不可分辨程度依赖于由数据生成的初始等价关系，因此对形成最终聚类的不可分辨程度的推断还没有得到很好的研究。在这项工作中，我们试图通过引入完美的初始等价关系来避免初始等价关系的影响，该关系由每个对象的原始类定义，以及它们的随机变异变体。然后研究了不可区分度阈值与结果簇之间的关系。结果表明，根据不可分辨程度与聚类个数的关系曲线，可以确定最佳不可分辨范围。
In this paper, we investigate the nature of indiscernibility degree which is a primary parameter in rough clustering. Indiscernibility degree of two objects represents the ratio of equivalence relations that classify them into the same category. As it reflects the global commonality in discriminating objects, it can be associated with the coarseness of classification knowledge. Rough clustering utilizes indiscernibility degree in the process of iterative refinement of initial equivalence relations, in order to control the coarseness of the resultant clusters. However, the inference of indiscernibility degree on forming final clusters has not been well investigated, as it depends on the initial equivalence relations generated from data. In this work, we tried to seclude the influence of initial equivalence relations by introducing perfect initial equivalence relations, defined by the original class of each object, and their randomly mutated variants. Then we investigated the relationships between the threshold of indiscernibility degree and resultant clusters. The result suggested that the best range of indiscernibility degree could be determined according to the curve of indiscernibility degree and the number of clusters.
利用粗糙聚类从非欧相异矩阵构造聚类
DOI：
--
发表时间：
2006
期刊：
Lecture Notes in Artificial Intelligence 4012
影响因子：
--
作者：
S.Hirano;S.Tsumoto
通讯作者：
S.Tsumoto