Abstract We propose a definition of granular count realized in the presence of uncertain data modeled through possibility distributions. We show that the resulting counts are fuzzy intervals in the domain of natural numbers. Based on this result, we devise two algorithms for granular counting: an exact counting algorithm with quadratic-time complexity and an approximate counting algorithm with linear-time complexity. We compare the two algorithms on synthetic data and show their application to a Bioinformatics scenario concerning the assessment of gene expressions in cells.

中文翻译：

---

不确定数据的粒度计数

摘要 我们提出了在存在通过可能性分布建模的不确定数据的情况下实现的粒度计数的定义。我们表明结果计数是自然数域中的模糊区间。基于此结果，我们设计了两种粒度计数算法：具有二次时间复杂度的精确计数算法和具有线性时间复杂度的近似计数算法。我们在合成数据上比较了两种算法，并展示了它们在生物信息学场景中关于细胞基因表达评估的应用。