Updating multigranulation rough approximations with increasing of granular structures
Updating multigranulation rough approximations with increasing of granular structures
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DOI：
10.1016/j.knosys.2014.03.021
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Xibei Yang;Yong Qi;Hualong Yu;Xiaoning Song;Jing-yu Yang
中科院分区：
文献类型：
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作者：
Xibei Yang;Yong Qi;Hualong Yu;Xiaoning Song;Jing-yu Yang
在信息爆炸的时代，数据以前所未有的速度增长，粗糙近似的动态更新是粗糙集理论成功的关键因素。虽然许多更新方案已被提出来研究这类问题，他们很少在多粒度环境中进行。为了填补这一空白，本文首先探讨了多粒度粗糙近似的更新问题。提出了随着粒度结构的增加而更新多粒度粗糙近似的简单快速算法。与朴素算法不同，该算法是基于多粒度粗糙近似的单调性设计的。在6个微阵列数据集上的实验表明，在高维数据集上，该快速算法与朴素算法相比，能够有效地减少计算时间。此外，它也表明，快速算法是有用的，以减少计算时间的传统约简和基于属性聚类的约简。
Dynamic updating of the rough approximations is a critical factor for the success of the rough set theory since data is growing at an unprecedented rate in the information-explosion era. Though many updating schemes have been proposed to study such problem, few of them were carried out in a multigranulation environment. To fill such gap, the updating of the multigranulation rough approximations is firstly explored in this paper. Both naive and fast algorithms are presented for updating the multigranulation rough approximations with the increasing of the granular structures. Different from the naive algorithm, the fast algorithm is designed based on the monotonic property of the multigranulation rough approximations. Experiments on six microarray data sets show us that the fast algorithm can effectively reduce the computational time in comparison with the naive algorithm when facing high dimensional data sets. Moreover, it is also shown that fast algorithm is useful in decreasing the computational time of finding both traditional reduct and attribute clustering based reduct.