Noisy name datasets

These test datasets focuses on string data, which represent persons’ names. These datasets were generated using NSDGen (Noisy String Data Generator) tool. The total amount of elements obtained in each dataset is calculated as the product of k value and the number of exact duplicates. As noise is introduced, duplicates are no longer exact and that amount is referred from now on as the number of observations per group. We refer to noise when common typos as insertions, deletions, substitutions and/or transpositions of characters in strings are introduced. To introduce such typos in strings, we consider the graph of distances among keys in QWERTY keyboards. Them has been used to evaluate clustering algorithms based on partitions in the ambiguous name problem, record linkage and authority control files, as testing datasets.本系列测试数据集聚焦于代表个人姓名的字符串数据。该系列数据集通过NSDGen（Noisy String Data Generator，即噪声字符串数据生成器）工具生成。 每个数据集的总元素量按k值与精确重复项数量的乘积计算得出。由于引入了噪声，重复项不再为精确重复，因此该数值此后被称为每组观测数。 此处所称的噪声，指在字符串中引入常见拼写错误，包括字符的插入、删除、替换以及换位操作。为生成此类字符串拼写错误，我们以QWERTY键盘按键间的距离图作为依据。 本系列数据集已被用作测试数据集，用于评估针对歧义姓名问题、记录链接及权威控制文件的基于划分的聚类算法。