inst/app/ui/mod_cs_cluster_factoextra/hopkins_stats.md

In chriszheng2016/zstexplorer: Explorer for China Stock Market Investment

The Hopkins statistic is used to assess the clustering tendency of a data set by measuring the probability that a given data set is generated by a uniform data distribution. In other words, it tests the spatial randomness of the data.

The null and the alternative hypotheses are defined as follow:

• Null hypothesis: the data set D is uniformly distributed (i.e., no meaningful clusters)

• Alternative hypothesis: the data set D is not uniformly distributed (i.e., contains meaningful clusters)

Rule to judge:

• If the value of Hopkins statistic is close to 1 (far above 0.5), the dataset is significantly clusterable.
• If the value of Hopkins statistic is close to 0.5, the dataset is uniformly distributed.

chriszheng2016/zstexplorer documentation built on June 13, 2021, 9:47 a.m.