Information fusion is capable of fusing and transforming multiple data derived from different sources to provide a unified representation for centralized knowledge mining that facilitates effective decision-making, classification and prediction, etc. Multisource interval-valued data, characterizing the uncertainty phenomena in the data in the form of intervals in different sources, are the most common symbolic data which widely exist in many real-world applications. This paper concentrates on efficient fusing of multisource interval-valued data with the dynamic updating of data sources, involving the addition of new sources and deletion of obsolete sources. We propose a novel data fusion method based on fuzzy information granulation, which translates multisource interval-valued data into trapezoidal fuzzy granules. Given this effectively fusing capability, we develop incremental mechanisms and algorithms for fusing multisource interval-valued data with a dynamic variation of data sources. Finally, extensive experiments are carried out to verify the effectiveness of the proposed algorithms when comparing to six different fusion algorithms. Experimental results show that the proposed fusion method outperforms other related approaches. Furthermore, the proposed incremental fusion algorithms can reduce the computing overhead in comparison with the static fusion algorithm when adding and deleting multiple data sources.

中文翻译：

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模糊粒度的多源区间值数据动态融合


信息融合能够将不同来源的多种数据进行融合和转换，为集中式知识挖掘提供统一的表示，有利于有效的决策、分类和预测等。多源区间值数据，刻画数据中的不确定性现象。不同来源的区间形式是最常见的符号数据，广泛存在于许多现实世界的应用中。本文专注于多源区间值数据与数据源动态更新的有效融合，包括添加新源和删除过时源。我们提出了一种基于模糊信息粒化的新型数据融合方法，将多源区间值数据转换为梯形模糊粒。鉴于这种有效的融合能力，我们开发了增量机制和算法，用于将多源区间值数据与数据源的动态变化融合。最后，与六种不同的融合算法进行比较，进行了大量的实验来验证所提出算法的有效性。实验结果表明，所提出的融合方法优于其他相关方法。此外，在添加和删除多个数据源时，与静态融合算法相比，所提出的增量融合算法可以减少计算开销。