Head/Tail Breaks：一种新的重尾数据分类方法
Head/tail Breaks: A New Classification Scheme for Data with a Heavy-tailed Distribution
摘要：
本文介绍了一种新的分类方案-头/尾休息-为了找到分组或层次的数据与重尾分布。重尾分布是严重右偏的，头部有少数大值，尾部有大多数小值，通常以幂律、对数正态或指数函数为特征。例如，一个国家的人口通常以这种重尾方式分布，少数人（例如，20%）在农村和绝大多数（例如，80%，在城市地区。这种重尾分布也被称为缩放、层次或缩放层次。这种新的分类方案将平均值周围的所有数据值分为两部分，并对头部中的值（高于平均值）迭代地继续该过程，直到头部值不再是重尾分布。因此，类的数量和类间隔都是自然确定的。因此，我们认为，新的分类方案是更自然的比自然的休息，在寻找分组或层次结构的数据与重尾分布。我们证明了头/尾休息方法的优势，在捕捉底层层次的数据Jenks的自然休息。关键词：数据分类、头/尾划分规则、自然间断、缩放和层次
英文摘要：
This paper introduces a new classification scheme - head/tail breaks - in order to find groupings or hierarchy for data with a heavy-tailed distribution. The heavy-tailed distributions are heavily right skewed, with a minority of large values in the head and a majority of small values in the tail, commonly characterized by a power law, a lognormal or an exponential function. For example, a country's population is often distributed in such a heavy-tailed manner, with a minority of people (e.g., 20 percent) in the countryside and the vast majority (e.g., 80 percent) in urban areas. This heavy-tailed distribution is also called scaling, hierarchy or scaling hierarchy. This new classification scheme partitions all of the data values around the mean into two parts and continues the process iteratively for the values (above the mean) in the head until the head part values are no longer heavy-tailed distributed. Thus, the number of classes and the class intervals are both naturally determined. We therefore claim that the new classification scheme is more natural than the natural breaks in finding the groupings or hierarchy for data with a heavy-tailed distribution. We demonstrate the advantages of the head/tail breaks method over Jenks' natural breaks in capturing the underlying hierarchy of the data. Keywords: data classification, head/tail division rule, natural breaks, scaling, and hierarchy
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
