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[book_chic.git] / chapter2.tex

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\chapter{From the founding situations of the SIA to its formalization}
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\abstract{
Starting from mathematical didactic situations, the implicitative
statistical analysis method develops as problems are encountered and
questions are asked.
Its main objective is to structure data crossing subjects and
variables, to extract inductive rules between variables and, based on
the contingency of these rules, to explain and therefore forecast in
various fields: psychology, sociology, biology, etc.
It is for this purpose that the concepts of intensity of implication,
class cohesion, implication-inclusion, significance of hierarchical
levels, contribution of additional variables, etc., are based.
Similarly, the processing of binary variables (e.g., descriptors) is
gradually being supplemented by the processing of modal, frequency
and, recently, interval and fuzzy variables.
}

\section*{Preamble}

Human operative knowledge is mainly composed of two components: that
of facts and that of rules between facts or between rules themselves.
It is his learning that, through his culture and his personal
experiences, allows him to gradually develop these forms of knowledge,
despite the regressions, the questioning, the ruptures that arise at
the turn of decisive information.
However, we know that these dialectically contribute to ensuring a
balanced operation.
However, the rules are inductively formed in a relatively stable way
as soon as the number of successes, in terms of their explanatory or
anticipatory quality, reaches a certain level (of confidence) from
which they are likely to be implemented.
On the other hand, if this (subjective) level is not reached, the
individual's economy will make him resist, in the first instance, his
abandonment or criticism.
Indeed, it is costly to replace the initial rule with another rule
when a small number of infirmations appear, since it would have been
reinforced by a large number of confirmations.
An increase in this number of negative instances, depending on the
robustness of the level of confidence in the rule, may lead to its
readjustment or even abandonment.
Laurent Fleury~\cite{Fleury}, in his thesis, correctly cites the
example - which Régis repeats - of the highly admissible rule: "all
Ferraris are red".
This very robust rule will not be abandoned when observing a single or
two counter-examples.
Especially since it would not fail to be quickly
re-comforted.

Thus, contrary to what is legitimate in mathematics, where not all
rules (theorem) suffer from exception, where determinism is total,
rules in the human sciences, more generally in the so-called "soft"
sciences, are acceptable and therefore operative as long as the number
of counter-examples remains "bearable" in view of the frequency of
situations where they will be positive and effective.
The problem in data analysis is then to establish a relatively
consensual numerical criterion to define the notion of a level of
confidence that can be adjusted to the level of requirement of the
rule user.
The fact that it is based on statistics is not surprising.
That it has a property of non-linear resistance to noise (weakness of
the first counter-example(s)) may also seem natural, in line with the
"economic" meaning mentioned above.
That it collapses if counter-examples are repeated also seems to have
to guide our choice in the modeling of the desired criterion.
This text presents the epistemological choice we have made.
As such it is therefore refutable, but the number of situations and
applications where it has proved relevant and fruitful leads us to
reproduce its genesis here.

\section{Introduction}

Different theoretical approaches have been adopted to model the
extraction and representation of imprecise (or partial) inference
rules between binary variables (or attributes or characters)
describing a population of individuals (or subjects or objects).
But the initial situations and the nature of the data do not change
the initial problem.
It is a question of discovering non-symmetrical inductive rules to
model relationships of the type "if a then almost b".
This is, for example, the option of Bayesian networks~\cite{Amarger}
or Galois lattices~\cite{Simon}.
But more often than not, however, since the correlation and the
${\chi}^2$ test are unsuitable because of their symmetric nature,
conditional probability~\cite{Loevinger, Agrawal,Grasn}  remains the
driving force behind the definition of the association, even when the
index of this selected association is multivariate~\cite{Bernard}.



Moreover, to our knowledge, on the one hand, most often the different
and interesting developments focus on proposals for a partial
implication index for binary data~\cite{Lermana} or \cite{Lallich}, on
the other hand, this notion is not extended to other types of
variables, to extraction and representation according to a rule graph
or a hierarchy of meta-rules; structures aiming at access to the
meaning of a whole not reduced to the sum of its
parts~\cite{Seve}\footnote{This is what the philosopher L. Sève
  emphasizes :"... in the non-additive, non-linear passage of the
  parts to the whole, there are properties that are in no way
  precontained in the parts and which cannot therefore be explained by
  them" }, i.e. operating as a complex non-linear system.
For example, it is well known, through usage, that the meaning of a
sentence does not completely depend on the meaning of each of the
words in it (see the previous chapter, point 4).

Let us return to what we believe is fertile in the approach we are
developing.
It would seem that, in the literature, the notion of implication index
is also not extended to the search for subjects and categories of
subjects responsible for associations.
Nor that this responsibility is quantified and thus leads to a
reciprocal structuring of all subjects, conditioned by their
relationships to variables.
We propose these extensions here after recalling the founding
paradigm.


\section{Implication intensity in the binary case}

\subsection{Fundamental and founding situation}

A set of objects or subjects E is crossed with variables
(characters, criteria, successes,...) which are interrogated as
follows: "to what extent can we consider that instantiating variable\footnote{Throughout the book, the word "variable" refers to both an isolated variable in premise (example: "to be blonde") or a conjunction of isolated variables (example: "to be blonde and to be under 30 years old and to live in Paris")} $a$
implies instantiating variable $b$?
In other words, do the subjects tend to be $b$ if we know that they are
$a$?".
In natural, human or life sciences situations, where theorems (if $a$
then $b$) in the deductive sense of the term cannot be established
because of the exceptions that taint them, it is important for the
researcher and the practitioner to "mine into his data" in order to
identify sufficiently reliable rules (kinds of "partial theorems",
inductions) to be able to conjecture\footnote{"The exception confirms the rule", as the popular saying goes, in the sense that there would be no exceptions if there were no rule} a possible causal relationship,
a genesis, to describe, structure a population and make the assumption
of a certain stability for descriptive and, if possible, predictive
purposes.
But this excavation requires the development of methods to guide it
and to free it from trial and error and empiricism.


\subsection{Mathematization}

To do this, following the example of the I.C. Lerman similarity
measurement method \cite{Lerman,Lermanb}, following the classic
approach in non-parametric tests (e. g. Fischer, Wilcoxon, etc.), we
define~\cite{Grasb,Grasf} the confirmatory quality measure of the
implicative relationship $a \Rightarrow b$ from the implausibility of
the occurrence in the data of the number of cases that invalidate it,
i.e. for which $a$ is verified without $b$ being verified. This
amounts to comparing the difference between the quota and the
theoretical if only chance occurred\footnote{"...[in agreement with
    Jung] if the frequency of coincidences does not significantly
  exceed the probability that they can be calculated by attributing
  them solely by chance to the exclusion of hidden causal
  relationships, we certainly have no reason to suppose the existence
  of such relationships.", H. Atlan~\cite{Atlana}}.
But when analyzing data, it is this gap that we take into account and
not the statement of a rejection or null hypothesis eligibility.
This measure is relative to the number of data verifying $a$ and not
$b$ respectively, the circumstance in which the involvement is
precisely put in default.
It quantifies the expert's "astonishment" at the unlikely small number
of counter-examples in view of the supposed independence between the
variables and the numbers involved.

Let us be clear. A finite set $V$ of $v$ variables is given: $a$, $b$,
$c$,...
In the classical paradigmatic situation and initially retained, it is
about the performance (success-failure) to items of a questionnaire.
To a finite set $E$ of $n$ subjects $x$, functions of the type : $x
\rightarrow a(x)$ where $a(x) = 1$ (or $a(x) = true$) if $x$ satisfies
or has the character $a$ and $0$ (or $a(x) = false$) otherwise are
associated by abuse of writing.
In artificial intelligence, we will say that $x$ is an example or an
instance for $a$ if $a(x) = 1$ and a counter-example if not.


The $a \Rightarrow b$ rule is logically true if for any $x$ in the
sample, $b(x)$ is null only if $a(x)$ is also null; in other words if
set $A$ of the $x$ for which $a(x)=1$ is contained in set $B$ of the
$x$ for which $b(x)=1$.
However, this strict inclusion is only exceptionally observed in the
pragmatically encountered experiments.
In the case of a knowledge questionnaire, we could indeed observe a
few rare students passing an item $a$ and not passing item $b$,
without contesting the tendency to pass item $b$ when we have passed
item $a$.
With regard to the cardinals of $E$ (of size $n$), but also of $A$ (or
$n_a$) and $B$ (or $n_b$), it is therefore the "weight" of the
counter-examples (or) that must be taken into account in order to
statistically accept whether or not to keep the quasi-implication or
quasi-rule  $a \Rightarrow b$.  Thus, it is from the dialectic of
example-counter-examples that the rule appears as the overcoming of
contradiction.

\subsection{Formalization}

To formalize this quasi-rule, we consider any two parts $X$ and $Y$ of
$E$, chosen randomly and independently (absence of a priori link
between these two parts) and of the same respective cardinals as $A$
and $B$. Let $\overline{Y}$ and $\overline{B}$ be the respective complementary of $Y$ and $B$ in $E$ of the same cardinal $n_{\overline{b}}= n-n_b$.

We will then say:

\definition $a \Rightarrow b$ is acceptable at confidence level
$1-\alpha$ if and only if
$$Pr[Card(X\cap \overline{Y})\leq card(A\cap \overline{B})]\leq \alpha$$

\begin{figure}[htbp]
  \centering
\includegraphics[scale=0.34]{chap2fig1.png}
 \caption{The dark grey parts correspond to the counter-examples of the
   implication $a \Rightarrow b$}
\label{chap2fig1}      
\end{figure}

It is established \cite{Lermanb} that, for a certain drawing process,
the random variable $Card(X\cap \overline{Y})$ follows the Poisson law
of parameter $\frac{n_a n_{\overline{b}}}{n}$.
We achieve this same result by proceeding differently in the following
way:

Note $X$ (resp. $Y$) the random subset of binary transactions where
$a$ (resp. $b$) would appear, independently, with the frequency
$\frac{n_a}{n}$ (resp. $\frac{n_b}{n}$).
To specify how the transactions specified in variables $a$ and $b$,
respectively $A$ and $B$, are extracted, for example, the following
semantically permissible assumptions are made regarding the
observation of the event: $[a=1~ and~ b=0]$. $(A\cap
\overline{B})$\footnote{We then note $\overline{v}$ the variable
  negation of $v$  (or $not~ v$) and $\overline{P}$ the complementary
  part of the part P of E.} is the subset of transactions,
counter-examples of implication $a \Rightarrow b$: 

Assumptions:
\begin{itemize}
\item h1: the waiting times of an event $[a~ and~ not~ b]$ are independent
  random variables;
\item h2: the law of the number of events occurring in the time
  interval $[t,~ t+T[$ depends only on T;
\item h3: two such events cannot occur simultaneously
\end{itemize}

It is then demonstrated (for example in~\cite{Saporta}) that the
number of events occurring during a period of fixed duration $n$
follows a Poisson's law of parameter $c.n$ where $c$ is called the
rate of the apparitions process during the unit of time.


However, for each transaction assumed to be random, the event $[a=1]$
has the probability of the frequency $\frac{n_a}{n}$, the event[b=0]
has as probability the frequency, therefore the joint event $[a=1~
  and~ b=0]$ has for probability estimated by the frequency
$\frac{n_a}{n}. \frac{n_{\overline{b}}}{b}$ in the hypothesis of absence of an a priori link between a and b (independence).

We can then estimate the rate $c$ of this event by $\frac{n_a}{n}. \frac{n_{\overline{b}}}{b}$.

Thus for a duration of time $n$, the occurrences of the event $[a~ and~ not~b]$ follow a Poisson's law of parameter : 
$$\lambda = \frac{n_a.n_{\overline{b}}}{n}$$

As a result, $Pr[Card(X\cap \overline{Y})= s]= e^{-\lambda}\frac{\lambda^s}{s!}$

Consequently, the probability that the hazard will lead, under the
assumption of the absence of an a priori link between $a$ and $b$, to
more counter-examples than those observed is:

$$Pr[Card(X\cap \overline{Y})\leq card(A\cap \overline{B})] =
\sum^{card(A\cap \overline{B})}_{s=0}  e^{-\lambda}\frac{\lambda^s}{s!} $$

 But other legitimate drawing processes lead to a binomial law, or
 even a hypergeometric law (itself not semantically adapted to the
 situation because of its symmetry). Under suitable convergence
 conditions, these two laws are finally reduced to the Poisson Law
 above (see Annex to this chapter).
 
If $n_{\overline{b}}\neq 0$, we reduce and center this Poison variable
into the variable:

$$Q(a,\overline{b})= \frac{card(X \cap \overline{Y})) -  \frac{n_a.n_{\overline{b}}}{n}}{\sqrt{\frac{n_a.n_{\overline{b}}}{n}}}  $$

In the experimental realization, the observed value of
$Q(a,\overline{b})$ is $q(a,\overline{b})$.
It estimates a gap between the contingency $(card(A\cap
\overline{B}))$ and the value it would have taken if there had been
independence between $a$ and $b$.

\definition
\begin{equation} q(a,\overline{b}) = \frac{n_{a \wedge \overline{b}}-
    \frac{n_a.n_{\overline{b}}}{n}}{\sqrt{\frac{n_a.n_{\overline{b}}}{n}}}
  \label{eq2.1}
\end{equation}  
is called the implication index, the number used as an indicator of
the non-implication of $a$ to $b$.
In cases where the approximation is properly legitimized (for example
$\frac{n_a.n_{\overline{b}}}{n}\geq 4$), the variable
$Q(a,\overline{b})$ approximately follows the reduced centered normal
distribution. The intensity of implication, measuring the quality of
$a\Rightarrow b$, for $n_a\leq n_b$ and  $nb \neq n$, is then defined
from the index $q(a,\overline{b})$ by:

\definition 
The implication intensity  that measures the inductive quality of a
over b is:
$$\varphi(a,b)=1-Pr[Q(a,\overline{b})\leq q(a,\overline{b})] =
\frac{1}{\sqrt{2 \pi}} \int^{\infty}_{ q(a,\overline{b})}
e^{-\frac{t^2}{2}} dt,~ if~ n_b \neq n$$
$$\varphi(a,b)=0,~ otherwise$$
As a result, the definition of statistical implication becomes:
\definition 
Implication  $a\Rightarrow b$ is admissible at confidence level
$1-\alpha $ if and only if: 
$$\varphi(a,b)\geq 1-\alpha$$


It should be recalled that this modeling of quasi-implication measures
the astonishment to note the smallness of counter-examples compared to
the surprising number of instances of implication.
It is a measure of the inductive and informative quality of
implication. Therefore, if the rule is trivial, as in the case where
$B$ is very large or coincides with $E$, this astonishment becomes
small.
We also demonstrate~\cite{Grasf} that this triviality results in a
very low or even zero intensity of implication: If, $n_a$ being fixed
and $A$ being included in $B$, $n_b$ tends towards $n$ ($B$ "grows"
towards $E$), then $\varphi(a,b)$ tends towards $0$. We therefore
define, by "continuity":$\varphi(a,b) = 0$ if $n_b = n$. Similarly, if
$A\subset B$, $\varphi(a,b)$ may be less than $1$ in the case where
the inductive confidence, measured by statistical surprise, is
insufficient.

{\bf \remark Total correlation, partial correlation}


We take here the notion of correlation in a more general sense than
that used in the domain that develops the linear correlation
coefficient (linear link measure) or the correlation ratio (functional
link measure).
In our perspective, there is a total (or partial) correlation between
two variables $a$ and $b$ when the respective events they determine
occur (or almost occur) at the same time, as well as their opposites.
However, we know from numerical counter-examples that correlation and
implication do not come down to each other, that there can be
correlation without implication and vice versa~\cite{Grasf} and below.
If we compare the implication coefficient and the linear correlation
coefficient algebraically, it is clear that the two concepts do not
coincide and therefore do not provide the same
information\footnote{"More serious is the logical error inferred from
  a correlation found to the existence of a causality" writes Albert
  Jacquard in~\cite{Jacquard}, p.159. }.

The quasi-implication of non-symmetric index $q(a,\overline{b})$ does
not coincide with the correlation coefficient $\rho(a, b)$ which is
symmetric and which reflects the relationship between variables a and
b. Indeed, we show~\cite{Grasf} that if $q(a,\overline{b}) \neq 0$
then
$$\frac{\rho(a,b)}{q(a,\overline{b})} = \sqrt{\frac{n}{n_b
    n_{\overline{a}}}} q(a,\overline{b})$$
With the correlation considered from the point of view of linear
correlation, even if correlation and implication are rather in the
same direction, the orientation of the relationship between two
variables is not transparent because it is symmetrical, which is not
the bias taken in the SIA.
From a statistical relationship given by the correlation, two opposing
empirical propositions can be deduced.

The following dual numerical situation clearly illustrates this:


\begin{table}[htp]
\center
\begin{tabular}{|l|c|c|c|}\hline
\diagbox[width=4em]{$a_1$}{$b_1$}&
  1 & 0 & marge\\ \hline
  1 & 96 & 4& 100 \\ \hline
  0 & 50 & 50& 100 \\ \hline
  marge & 146 & 54& 200 \\ \hline
\end{tabular} ~ ~ ~ ~ ~ ~ ~ \begin{tabular}{|l|c|c|c|}\hline
\diagbox[width=4em]{$a_2$}{$b_2$}&
  1 & 0 & marge\\ \hline
  1 & 94 & 6& 100 \\ \hline
  0 & 52 & 48& 100 \\ \hline
  marge & 146 & 54& 200 \\ \hline
\end{tabular}

\caption{Numeric example of difference between implication and
  correlation}
\label{chap2tab1}
\end{table}

In Table~\ref{chap2tab1}, the following correlation and implications
can be computed:\\
Correlation $\rho(a_1,b_1)=0.468$, Implication
$q(a_1,\overline{b_1})=-4.082$\\
Correlation $\rho(a_2,b_2)=0.473$, Implication  $q(a_2,\overline{b_2})=-4.041$


Thus, we observe that, on the one hand, $a_1$ and $b_1$ are less
correlated than $a_2$ and $b_2$ while, on the other hand, the
implication intensity of $a_1$ over $b_1$ is higher than that of $a_2$
over $b_2$ since $q1 <q2$.

On this subject, Alain Ehrenberg in~\cite{Ehrenberg} writes: "The
finding of a correlation does not remove the ambiguity between" when I do $X$, my brain is in state $Y$" and "if I do $X$, it is because my brain is in state $Y$", that is, between something that happens in my brain when I do an action.

\remark  Remember that we consider not only conjunctions of variables
of the type "$a$ and $b$" but also disjunctions such as "($a$ and $b$)
or $c$..." in order to model phenomena that are concepts as it is done
in learning or in artificial intelligence.
The associated calculations remain compatible with the logic of the
proposals linked by connectors.

\remark Unlike the Loevinger Index~\cite{Loevinger}  and conditional
probability $(Pr[B/A])=1$ and all its derivatives, the implication
intensity varies, non-linearly, with the expansion of sets $E$, $A$
and $B$ and weakens with triviality (see Definition 2.3).
Moreover, it
is resistant to noise, especially around $0$ for, which can only make
the relationship we want to model and establish statistically
credible.
Finally, as we have seen, the inclusion of $A$ in $B$ does not ensure
maximum intensity, the inductive quality may not be strong, whereas
$Pr[B/A]$ is equal to $1$~\cite{Grasm,Guillet}.
In paragraph 5, we study more closely the problem of the sensitivity
and stability of the implication index as a function of small
variations in the parameters involved in the study of its
differential.

\section{Case of modal and frequency variables}
\subsection{Founding situation}

Marc Bailleul's (1991-1994) research focuses in particular on the
representation that mathematics teachers have of their own teaching.
In order to highlight it, meaningful words are proposed to them that
they must prioritize.
Their choices are no longer binary, the words chosen by any teacher
are ordered at least at the most representative.
Mr. Bailleul's question then focuses on questions of the type: "if I
choose this word with this importance, then I choose this other word
with at least equal importance".
It was therefore necessary to extend the notion of statistical
implication to variables other than binary.
This is the case for modal variables that are associated with
phenomena where the values $a(x)$ are numbers in the interval $[0, 1]$
and describe degrees of belonging or satisfaction as are fuzzy logic,
for example, linguistic modifiers "maybe", "a little", "sometimes",
etc.
This problem is also found in situations where the frequency of a
variable reflects a preorder on the values assigned by the subjects to
the variables presented to them.
These are frequency variables that are associated with phenomena where
the values of $a(x)$ are any positive real values.
This is the case when one considers a student's percentage of success
in a battery of tests in different areas.

\subsection{Formalization}

J.B. Lagrange~\cite{Lagrange} has demonstrated that, in the modal
case,
\begin{itemize}
  \item if $a(x)$ and $\overline{b}(x)$ are the values taken at $x$ by
    the modal variables $a$ and $\overline{b}$, with $(x)=1-b(x)$
  \item if $s^2_a$ and $s_{\overline{b}}^2$ are the empirical variances of variables $a$ and $\overline{b}$
then  the implication index, which he calls propensity index, becomes: 

\definition
$$q(a,\overline{b}) = \frac{\sum_{x\in E} a(x)\overline{b}(x)  -
  \frac{n_a n_{\overline{b}}}{n}}
{\sqrt{\frac{(n^2s_a^2+n_a^2)(n^2+s_{\overline{b}}^2 + n_{\overline{b}}^2)}{n^3}}}$$
is the index of propensity of modal variables.
\end{itemize}

J.B. Lagrange also proves that this index coincides with the index
defined previously in the binary case if the number of modalities of a
and b is precisely 2, because in this case :\\
$n^2s_a^2+n_a^2=n n_a$,~ ~ $ n^2+s_{\overline{b}}^2 + n_{\overline{b}}=n
  n_{\overline{b}}$~ ~ and ~ ~ $\sum_{x\in E} a(x)\overline{b}(x)=n_{a \wedge
  \overline{b}}$.

 This solution provided in the modal case is also applicable to the
 case of frequency variables, or even positive numerical variables,
 provided that the values observed on the variables, such as a and b,
 have been normalized, the normalization in $[0, 1]$ being made from the maximum of the value taken respectively by $a$ and $b$ on set $E$.

\remark
In~\cite{Regniera}, we consider rank variables that reflect a
total order between choices presented to a population of judges.
Each of them must order their preferential choice among a set of
objects or proposals made to them.
An index measures the quality of the statement of the type: "if object
$a$ is ranked by judges then, generally, object $b$ is ranked higher
by the same judges".
Proximity to the previous issue leads to an index that is relatively
close to the Lagrange index, but better adapted to the rank variable
situation.


\section{Cases of variables-on-intervals  and interval-variables}
\subsection{Variables-on-intervals}
\subsubsection{Founding situation}

For example, the following rule is sought to be extracted from a
biometric data set, estimating its quality: "if an individual weighs
between $65$ and $70kg$ then in general he is between $1.70$ and
$1.76m$ tall".
A similar situation arises in the search for relationships between
intervals of student performance in two different subjects.
The more general situation is then expressed as follows: two real
variables $a$ and $b$ take a certain number of values over 2 finite
intervals $[a1,~ a2]$ and $[b1,~ b2]$. Let $A$ (resp. $B$) be all the
values of $a$ (resp. $b$) observed over $[a1,~ a2]$ (resp. $[b1,~
  b2]$).
For example, here, a represents the weights of a set of n subjects and b the sizes of these same subjects.

Two problems arise:
\begin{enumerate}
\item  Can adjacent sub-intervals of $[a1,~ a2]$ (resp. $[b1,~ b2]$)
  be defined so that the finest partition obtained best respects the
  distribution of the values observed in $[a1,~ a2]$ (resp. $[b1,~ b2]$)?
\item  Can we find the respective partitions of $[a1,~ a2]$ and $[b1,~
  b2]$ made up of meetings of the previous adjacent sub-intervals,
  partitions that maximize the average intensity of involvement of the
  sub-intervals of one on sub-intervals on the other belonging to
  these partitions?
\end{enumerate}

We answer these two questions as part of our problem by choosing the
criteria to optimize in order to satisfy the optimality expected in
each case.
To the first question, many solutions have been provided in other
settings (for example, by~\cite{Lahaniera}).

\subsubsection{First problem}

We will look at the interval $[a1,~ a2]$ assuming it has a trivial
initial partition of sub-intervals of the same length, but not
necessarily of the same frequency distribution observed on these
sub-intervals.
Note $P_0 = \{A_{01},~ A_{02},~ ...,~ A_{0p}\}$, this partition in $p$
sub-intervals.
We try to obtain a partition of $[a1,~ a2]$ into $p$ sub-intervals
$\{A_{q1},~ A_{q2},~ ...,~ A_{qp}\}$ in such a way that within each
sub-interval there is good statistical homogeneity (low intra-class
inertia) and that these sub-intervals have good mutual heterogeneity
(high inter-class inertia).
We know that if one of the criteria is verified, the other is
necessarily verified (Koenig-Huyghens theorem).
This will be done by adopting a method directly inspired by the
dynamic cloud method developed by Edwin Diday~\cite{Diday} (see also
\cite{Lebart} and adapted to the current situation. This results in
the optimal partition targeted.

\subsubsection{Second problem}

It is now assumed that the intervals $[a1,~ a2]$ and $[b1,~ b2]$ are
provided with optimal partitions $P$ and $Q$, respectively, in the
sense of the dynamic clouds.
Let $p$ and $q$ be the respective numbers of sub-intervals composing
$P$ and $Q$.
From these two partitions, it is possible to generate $2^{p-1}$ and
$2^{q-1}$ partitions obtained by iterated meetings of adjacent
sub-intervals of $P$ and $Q$ \footnote{It is enough to consider the tree structure of which $A_1$ is the root, then to join it or not to $A_2$ which itself will or will not be joined to $A_3$, etc. There are therefore $2^{p-1}$ branches in this tree structure.} respectively. 
We calculate the respective intensities of implication of each
sub-interval, whether or not combined with another of the first
partition, on each sub-interval, whether or not combined with another
of the second, and then the values of the intensities of the
reciprocal implications.
There are therefore a total of $2.2^{p-1}.2^{q-1}$ families of
implication intensities, each of which requires the calculation of all
the elements of a partition of $[a1,~ a2]$ on all the elements of one
of the partitions of $[b1,~ b2]$ and vice versa.
The optimality criterion is chosen as the geometric mean of the
intensities of implication, the mean associated with each pair of
partitions of elements, combined or not, defined inductively.
We note the two maxima obtained (direct implication and its
reciprocal) and we retain the two associated partitions by declaring
that the implication of the variable-on-interval $a$ on the
variable-on-interval $b$ is optimal when the interval $[a1,~ a2]$
admits the partition corresponding to the first maximum and that the
optimal reciprocal involvement is satisfied for the partition of
$[b1,~ b2]$ corresponding to the second maximum.

\subsection{Interval-variables}
\subsubsection{Founding situation}
Data are available from a population of $n$ individuals (who may be
each or some of the sets of individuals, e.g. a class of students)
according to variables (e.g. grades over a year in French, math,
physics,..., but also: weight, height, chest size,...).
The values taken by these variables for each individual are intervals
of positive real values.
For example, individual $x$ gives the value $[12,~ 15.50]$ to the math
score variable.
E. Diday would speak on this subject of symbolic variables $p$ at
intervals defined on the population.


We try to define an implication of intervals, relative to a variable
$a$, which are themselves observed intervals, towards other similarly
defined intervals and relative to another variable $b$.
This will make it possible to measure the implicit, and therefore
non-symmetric, association of certain interval(s) of the variable a
with certain interval(s) of the variable $b$, as well as the
reciprocal association from which the best one will be chosen for each
pair of sub-intervals involved, as just described in §4.1.

For example, it will be said that the sub-interval $[2, 5.5]$ of
mathematical scores generally implies the sub-interval $[4.25, 7.5]$
of physical scores, both of which belong to an optimal partition in
terms of the explained variance of the respective value ranges $[1,
  18]$ and $[3, 20]$ taken in the population.
Similarly, we will say that $[14.25, 17.80]$ in physics most often
implies $[16.40, 18]$ in mathematics. 


\subsubsection{Algorithm}

By following the problem of E. Diday and his collaborators, if the
values taken according to the subjects by the variables $a$ and $b$
are of a symbolic nature, in this case intervals of $\mathbb{R}^+$, it
is possible to extend the above algorithms\cite{Grasi}.
For example, variable $a$ has weight intervals associated with it and
variable $b$ has size intervals associated with variable $b$, due to
inaccurate measurements.
By combining the intervals $I_x$ and $J_x$ described by the subjects
$x$ of $E$ according to each of the variables $a$ and $b$
respectively, we obtain two intervals $I$ and $J$ covering all
possible values of $a$ and $b$.
On each of them a partition can be defined in a certain number of
intervals respecting as above a certain optimality criterion.
For this purpose, the intersections of intervals such as $I_x$ and
$J_x$ with these partitions will be provided with a distribution
taking into account the areas of the common parts.
This distribution may be uniform or of another discrete or continuous
type.
But thus, we are back in search of rules between two sets of
variables-on-intervals that take, as previously in §4.1, their values
on $[0,~ 1]$ from which we can search for optimal implications.


\remark Whatever the type of variable considered, there is often a
problem of overabundance of variables and therefore difficulty of
representation.
For this reason, we have defined an equivalence relationship on all
variables that allows us to substitute a so-called leader variable for
an equivalence class~\cite{Grask}.

\section{Variations in the implication index q according to the 4 occurrences}

In this paragraph, we examine the sensitivity of the implication index
to disturbances in its parameters.

\subsection{Stability of the implication index}
To study the stability of the implication index $q$ is to examine its
small variations in the vicinity of the $4$ observed integer values
($n$, $n_a$, $n_b$, $n_{a \wedge \overline{b}}$).
To do this, it is possible to perform different simulations by
crossing these 4 integer variables on which $q$ depends~\cite{Grasx}.
But let us consider these variables as variables with real values and
$q$ as a function that can be continuously differentiated from these
variables, which are themselves forced to respect inequalities: $0\leq
n_a \leq n_b$ and $n_{a \wedge \overline{b}} \leq inf\{n_a,~ n_b\}$ and
$sup\{n_a,~ n_b\} \leq n$.
The function $q$ then defines a scalar and vector field on
$\mathbb{R}^4$ as an affine and vector space on itself.
In the likely hypothesis of an evolution of a nonchaotic process of
data collection, it is then sufficient to examine the differential of
$q$ with respect to these variables and to keep its restriction to the
integer values of the parameters of the relationship $a \Rightarrow b$.
The differential of $q$, in the sense of Fréchet's
topology\footnote{Fréchet's topology allows $\mathbb{N}$ sections,
  i.e. subsets of naturals of the form $\{n,~ n+1,~ n+2,~ ....\}$, to be
  used as a filter base, while the usual topology on $\mathbb{R}$
  allows real intervals for filters.
  Thus continuity and derivability are perfectly defined and
  operational concepts according to Fréchet's topology in the same way
  as they are with the usual topology.}, is expressed as follows by
the scalar product:

\begin{equation}
dq = \frac{\partial q}{\partial n}dn + \frac{\partial q}{\partial
  n_a}dn_a +  \frac{\partial q}{\partial n_b}dn_b +  \frac{\partial
  q}{\partial n_{a \wedge \overline{b}}}dn_{a \wedge \overline{b}} =
grad~q.dM\footnote{By a mechanistic metaphor, we will say that $dq$ is
  the elementary work of $q$ for a movement $dM$ (see chapter 14 of
  this book).}
\label{eq2.2}
\end{equation}

where $M$ is the coordinate point $(n,~ n_a,~ n_b,~ n_{a \wedge
  \overline{b}})$ of the vector scalar field $C$, $dM$ is the
component vector the differential increases of these occurrence
variables, and $grad~ q$ the component vector the partial derivatives
of these occurrence variables.

The differential of the function $q$ therefore appears as the scalar product of its gradient and the increase of $q$ on the surface representing the variations of the function $q(n,~ n_a,~ n_b,~ n_{a \wedge
  \overline{b}})$. Thus, the gradient of $q$ represents its own
variations according to those of its components, the 4 cardinals of
the assemblies $E$, $A$, $B$ and $card(A\cap \overline{B})$. It
indicates the direction and direction of growth or decrease of $q$ in
the space of dimension 4. Remember that it is carried by the normal to
the surface of level $q~ =~ cte$.

If we want to study how $q$ varies according to $ n_{\overline{b}}$,
we just have to replace $n_b$ by $n-n_b$ and therefore change the sign
of the derivative of $n_b$ in the partial derivative. In fact, the
interest of this differential lies in estimating the increase
(positive or negative) of $q$ that we note $\Delta q$ in relation to
the respective variations $\Delta n$, $\Delta n_a$, $\Delta n_b$ and
$\Delta n_{a \wedge
  \overline{b}}$. So we have: 


$$\Delta q= \frac{\partial q}{\partial n} \Delta n + \frac{\partial
  q}{\partial n_a} \Delta n_a  + \frac{\partial
  q}{\partial n_b} \Delta n_b + \frac{\partial
  q}{\partial n_{a \wedge
  \overline{b}}} \Delta n_{a \wedge
  \overline{b}} +o(\Delta q)$$

where $o(\Delta q)$ is an infinitely small first order. 
Let us examine the partial derivatives of $n_b$ and  $n_{a \wedge
  \overline{b}}$ the number of counter-examples. We get:

\begin{equation}
  \frac{\partial
  q}{\partial n_b} = \frac{1}{2} n_{a \wedge
  \overline{b}} (\frac{n_a}{n})^{-\frac{1}{2}} (n-n_b)^{-\frac{3}{2}}
  + \frac{1}{2} (\frac{n_a}{n})^{\frac{1}{2}} (n-n_b)^{-\frac{1}{2}} >
  0
  \label{eq2.3}
\end{equation}


\begin{equation}
  \frac{\partial
  q}{\partial n_{a \wedge
  \overline{b}}}    = \frac{1}{\sqrt{\frac{n_a n_{\overline{b}}}{n}}}
  = \frac{1}{\sqrt{\frac{n_a (n-n_b)}{n}}} > 0
  \label{eq2.4}
\end{equation}


Thus, if the increases $\Delta nb$ and $\Delta n_{a \wedge
  \overline{b}}$ are positive, the increase of $q(a,\overline{b})$ is
also positive. This is interpreted as follows: if the number of
examples of $b$ and the number of counter-examples of implication
increase then the intensity of implication decreases for $n$ and $n_a$
constant. In other words, this intensity of implication is maximum at
observed values $n_b$ and $ n_{a \wedge
  \overline{b}}$ and minimum at values $n_b+\Delta n_b$ and  $n_{a \wedge
  \overline{b}}+ n_{a \wedge
  \overline{b}}$.

If we examine the case where $n_a$ varies, we obtain the partial
derivative of $q$ with respect to $n_a$ which is:

\begin{equation}
  C = \frac{ n_{a \wedge \overline{b}}}{2
  \sqrt{\frac{n_{\overline{b}}}{n}}}
  \left(\frac{n}{n_a}\right)^{\frac{3}{2}}
  -\frac{1}{2}\sqrt{\frac{n_{\overline{b}}}{n_a}}<0
  \label{eq2.5}
  \end{equation}

Thus, for variations of $n_a$ on $[0,~ nb]$, the implication index function is always decreasing (and concave) with respect to $n_a$ and is therefore minimum for $n_a= n_b$. As a result, the intensity of implication is increasing and maximum for $n_a= n_b$.

Note the partial derivative of $q$ with respect to $n$:

$$\frac{\partial q}{\partial n} = \frac{1}{2\sqrt{n}} \left(  n_{a
  \wedge \overline{b}}+\frac{n_a n_{\overline{b}}}{n}   \right)$$

Consequently, if the other 3 parameters are constant, the implication
index decreases by $\sqrt{n}$.
The quality of implication is therefore all the better, a specific
property of the SIA compared to other indicators used in the
literature~\cite{Grasab}.
This property is in accordance with statistical and semantic
expectations regarding the credit given to the frequency of
observations.
Since the partial derivatives of $q$ (at least one of them) are
non-linear according to the variable parameters involved, we are
dealing with a non-linear dynamic system\footnote{"Non-linear systems
  are systems that are known to be deterministic but for which, in
  general, nothing can be predicted because calculations cannot be
  made"~\cite{Ekeland} p. 265.} with all the epistemological
consequences that we will consider elsewhere.



\subsection{Numerical example}
In a first experiment, we observe the occurrences: $n = 100$, $n_a =
20$, $n_b = 40$ (hence $n_b=60$, $ n_{a   \wedge \overline{b}} = 4$).
The application of formula (\ref{eq2.1}) gives = -2.309.
In a 2nd experiment, $n$ and $n_a$ are unchanged but the occurrences
of $b$ and counter-examples $n_{a   \wedge \overline{b}}$ increase by one unit. 

At the initial point of the space of the 4 variables, the partial
derivatives that only interest us (according to $n_b$ and $n_{a
  \wedge \overline{b}}$) have respectively the following values when
applying formulas (\ref{eq2.3}) and (\ref{eq2.4}): $\frac{\partial
  q}{\partial n_b} = 0.0385$ and $\frac{\partial q}{\partial n_{a
  \wedge \overline{b}}}  = 0.2887$.

As $\Delta n_b$, $\Delta n_{\overline{b}}$ and $\Delta  n_{a
  \wedge \overline{b}} $ are equal to 1, -1 and 1, then $\Delta q$ is
equal to: $0.0385 + 0.2887 + o(\Delta q) = 0.3272 + o(\Delta q)$ and
the approximate value of $q$ in the second experiment is $-2.309 +
0.2887 + o(\Delta q)= -1.982 +o(\Delta q)$ using the first order
development of $q$ (formula (\ref{eq2.2})).
However, the calculation of the new implication index $q$ at the point
of the 2nd experiment is, by the use of (\ref{eq2.1}): $-1.9795$, a
value well approximated by the development of $q$.



\subsection{A first differential relationship of $\varphi$ as a function of function $q$}
Let us consider the intensity of implication $\varphi$ as a function
of $q(a,\overline{b})$: 
$$\varphi(q)=\frac{1}{\sqrt{2\pi}}\int_q^{\infty}e^{-\frac{t^2}{2}}$$
We can then examine how $\varphi(q)$ varies when $q$ varies in the neighberhood of a given value $(a,b)$, knowing how $q$ itself varies according to the 4 parameters that determine it. By derivation of the integration bound, we obtain:
\begin{equation}
  \frac{d\varphi}{dq}=-\frac{1}{\sqrt{2\pi}}e^{-\frac{q^2}{2}} < 0
  \label{eq2.6}
\end{equation}
This confirms that the intensity increases when $q$ decreases, but the growth rate is specified by the formula, which allows us to study more precisely the variations of $\varphi$. Since the derivative of $\varphi$ from $q$ is always negative, the function $\varphi$ is decreasing.

{\bf Numerical example}\\
Taking the values of the occurrences observed in the 2 experiments
mentioned above, we find for $q = -2.309$, the value of the intensity
of implication  $\varphi(q)$ is equal to 0.992. Applying formula
(\ref{eq2.6}), the derivative of $\varphi$ with respect to $q$ is:
-0.02775 and the negative increase in intensity is then: -0.02775,
$\Delta q$ = 0.3272. The approximate first-order intensity is
therefore: $0.992-\Delta q$ or 0.983. However, the actual calculation
of this intensity is, for $q= -1.9795$,  $\varphi(q) = 0.976$.



\subsection{Examination of other indices}
Unlike the core index $q$ and the intensity of implication, which
measures quality through probability (see definition 2.3), the other
most common indices are intended to be direct measures of quality.
We will examine their respective sensitivities to changes in the
parameters used to define these indices.
We keep the ratings adopted in paragraph 2.2 and select indices that
are recalled in~\cite{Grasm},~\cite{Lencaa}  and~\cite{Grast2}.

\subsubsection{The Loevinger Index}

It is an "ancestor" of the indices of
implication~\cite{Loevinger}. This index, rated $H(a,b)$, varies from
1 to $-\infty$. It is defined by: $H(a,b) =1-\frac{n n_{a \wedge
    b}}{n_a n_b}$. Its partial derivative with respect to the variable number of counter-examples is therefore:
$$\frac{\partial H}{\partial n_{a \wedge \overline{b}}}=-\frac{n}{n_a n_b}$$ 
Thus the implication index is always decreasing with $n_{a \wedge
  \overline{b}}$. If it is "close" to 1, implication is "almost"
satisfied. But this index has the disadvantage, not referring to a
probability scale, of not providing a probability threshold and being
invariant in any dilation of $E$, $A$, $B$ and $A \cap \overline{B}$.


\subsubsection{The Lift Index}

It is expressed by: $l =\frac{n n_{a \wedge b}}{n_a n_b}$.
This expression, linear with respect to the examples, can still be
written to highlight the number of counter-examples:
$$l =\frac{n (n_a - n_{a \wedge \overline{b}})}{n_a n_b}$$
To study the sensitivity of the $l$ to parameter variations, we use: 
$$\frac{\partial l}{\partial n_{a \wedge \overline{b}} } =
-\frac{1}{n_a n_b}$$
Thus, the variation of the Lift index is independent of the variation
of the number of counter-examples.
It is a constant that depends only on variations in the occurrences of $a$ and $b$. Therefore, $l$ decreases when the number of counter-examples increases, which semantically is acceptable, but the rate of decrease does not depend on the rate of growth of $n_{a \wedge \overline{b}}$.

\subsubsection{Confidence}

This index is the best known and most widely used thanks to the sound
box available in an Anglo-Saxon publication~\cite{Agrawal}.
It is at the origin of several other commonly used indices which are only variants satisfying this or that semantic requirement... Moreover, it is simple and can be interpreted easily and immediately. 
$$c=\frac{n_{a \wedge b}}{n_a} = 1-\frac{n_{a \wedge \overline{b}}}{n_a}$$

The first form, linear with respect to the examples, independent of
$n_b$, is interpreted as a conditional frequency of the examples of
$b$ when $a$ is known.
The sensitivity of this index to variations in the occurrence of
counter-examples is read through the partial derivative:
$$\frac{\partial c}{\partial n_{a \wedge \overline{b}} } =
-\frac{1}{n_a }$$


Consequently, confidence increases when $n_{a \wedge \overline{b}}$
decreases, which is semantically acceptable, but the rate of variation
is constant, independent of the rate of decrease of this number, of
the variations of $n$ and $n_b$.
This property seems not to satisfy intuition.
The gradient of $c$ is expressed only in relation to $n_{a \wedge
  \overline{b}}$ and $n_a$:(). {\bf CHECK FORMULA}
This may also appear to be a restriction on the role of parameters in
expressing the sensitivity of the index.

\section{Gradient field, implicative field}
We highlight here the existence of fields generated by the variables
of the corpus.

\subsection{Existence of a gradient field}
Like our Newtonian physical space, where a gravitational field emitted
by each material object acts, we can consider that it is the same
around each variable.
For example, the variable $a$ generates a scalar field whose value in
$b$ is maximum and equal to the intensity of implication or the
implicition index $q(a,\overline{b})$.
Its action spreads in V according to differential laws as J.M. Leblond
says, in~\cite{Leblond} p.242.

Let us consider the space $E$ of dimension 4 where the coordinates of
the points $M$ are the parameters relative to the binary variables $a$
and $b$, i.e. ($n$, $n_a$, $n_b$, $n_{a\wedge \overline{b}}$). $q(a,\overline{b})$ is the realization of a scalar field, as an application of $\mathbb{R}^4$ in $\mathbb{R}$ (immersion of $\mathbb{N}^4$ in $\mathbb{R}^4$). 
For the grad vector $q$ of components the partial derivatives of $q$
with respect to variables $n$, $n_a$, $n_b$, $n_{a\wedge
  \overline{b}}$ to define a gradient field - a particular vector
field that we will also call implicit field - it must respect the
Schwartz criterion of an exact total differential, i.e.:

$$\frac{\partial}{\partial n_{a\wedge   \overline{b}}}\left(
\frac{\partial q}{\partial n_b} \right) =\frac{\partial}{\partial n_b}\left(
\frac{\partial q}{\partial n_{a\wedge   \overline{b}}} \right) $$
and the same for the other variables taken in pairs. However, we have,
through the formulas (\ref{eq2.3}) and (\ref{eq2.4})