RT Journal Article
SR Electronic
T1 Hierarchical Association Coefficient Algorithm
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 043844
DO 10.1101/043844
A1 Bongsong Kim
YR 2016
UL http://biorxiv.org/content/early/2016/03/16/043844.1.abstract
AB Suppose that members in a universal set categorized based on observations, and that categories can be stratified based on the average of observations within each category. Two sorting extremes can be obtained from the perspective of arbitrariness of an order of observations. The first sorting extreme is an increasing order of observations on ascendingly stratified categories. The second sorting extreme is a decreasing order of observations on ascendingly stratified categories. Hierarchical association coefficient (HA-coefficient) algorithm is based on a principle that any order of observations in stratified categorization can be placed between the two sorting extremes. The algorithm produces a proportion of how much an order of observations in stratified categorization is close to the first sorting extreme, or how much an order of categorized observations is distant from the second sorting extreme. This paper introduces a theory about the HA-coefficient algorithm, and shows its applications with example data. In addition, proving a reliability of the algorithm is shown through a simulation.