TY - JOUR T1 - Hierarchical Association Coefficient Algorithm JF - bioRxiv DO - 10.1101/043844 SP - 043844 AU - Kim Bongsong Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/03/15/043844.abstract N2 - Suppose that members in a universal are categorized based on observations, and that categories can be stratified based on averages 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 showed its practices with example data. In addition, proving a reliability of the algorithm is shown through a simulation. ER -