  COMPASS is a longitudinal, prospective cohort study collecting data annually
from students attending high school in jurisdictions across Canada. We aimed to
discover significant frequent/rare associations of behavioral factors among
Canadian adolescents related to cannabis use. We use a subset of COMPASS
dataset which contains 18,761 records of students in grades 9 to 12 with 31
selected features (attributes) involving various characteristics, from living
habits to academic performance. We then used the Pattern Discovery and
Disentanglement (PDD) algorithm that we have developed to detect strong and
rare (yet statistically significant) associations from the dataset. PDD used
the criteria derived from disentangled statistical spaces (known as
Re-projected Adjusted-Standardized Residual Vector Spaces, notated as RARV). It
outperformed methods using other criteria (i.e. support and confidence) popular
as reported in the literature. Association results showed that PDD can
discover: i) a smaller set of succinct significant associations in clusters;
ii) frequent and rare, yet significant, patterns supported by population health
relevant study; iii) patterns from a dataset with extremely imbalanced groups
(majority class: minority class = 88.3%: 11.7%).
