classical definition, two events $ X{=}x$ and $C{=}c$ are statistically independent, if $P( X{=}x,C{=}c)=P(
X{=}x)P(C{=}c)$. Otherwise, they are considered dependent. A commonly used measure for the strength of this unconditional (or marginal) dependence is leverage $\delta( X{=}x,C{=}c)=P( X{=}x,C{=}c)-P(
X{=}x)P(C{=}c)$. When $X$ and $C$ are (composed or single) binary variables, $\delta(X,C)=\delta(\neg X,\neg C)=-\delta(X,\neg
C)=-\delta(\neg X,C)$, where $X$ and $C$ are used as shorthands for $X{=}1$ and $C{=}1$ and $\neg X$ and $\neg C$ as shorthands for $X{=}0$ and $C{=}0$ respectively. Therefore, the dependency can be represented by giving just one value pair, usually one expressing positive dependency.

In pattern discovery, statistical dependencies can be expressed as a type of association rules, which we call [*statistical association rules*]{} or [*dependency rules*]{} (see e.g., [@kingfkais]). Such rules express always statistical dependencies but, unlike traditional association rules, do not have any minimum frequency requirements. Instead, there are requirements for the strength and/or significance of statistical dependencies. We concentrate on dependency rules of the form $X\rightarrow C{=}c$, $c\in
\{0,1\}$, where $X$ is a set of positive valued binary attributes and $C$ is a single binary attribute. Rule $X\rightarrow C{=}1$ (or simply $X\rightarrow C$) expresses a positive dependency between $X$ and $C$ while $X\rightarrow C{=}0$ (or 