=Paper=
{{Paper
|id=Vol-1466/paper18
|storemode=property
|title=From an Implicational System to its Corresponding D-basis
|pdfUrl=https://ceur-ws.org/Vol-1466/paper18.pdf
|volume=Vol-1466
|dblpUrl=https://dblp.org/rec/conf/cla/LorenzoACEM15
}}
==From an Implicational System to its Corresponding D-basis==
<pdf width="1500px">https://ceur-ws.org/Vol-1466/paper18.pdf</pdf>
<pre>
            From an implicational system to its
                  corresponding D-basis

     Estrella Rodrı́guez-Lorenzo1 , Kira Adaricheva2 , Pablo Cordero1 , Manuel
                            Enciso1 , and Angel Mora1
                    1
                       University of Málaga, Andalucı́a Tech, Spain,
  e-mail: {estrellarodlor,amora}@ctima.uma.es, pcordero@uma.es, enciso@lcc.uma.es
                          2
                            Nazarbayev University, Kazakhstan
                            e-mail: kira.adaricheva@nu.edu.kz


        Abstract. Closure system is a fundamental concept appearing in several
        areas such as databases, formal concept analysis, artificial intelligence,
        etc. It is well-known that there exists a connection between a closure
        operator on a set and the lattice of its closed sets. Furthermore, the
        closure system can be replaced by a set of implications but this set has
        usually a lot of redundancy inducing non desired properties.
        In the literature, there is a common interest in the search of the mini-
        mality of a set of implications because of the importance of bases. The
        well-known Duquenne-Guigues basis satisfies this minimality condition.
        However, several authors emphasize the relevance of the optimality in
        order to reduce the size of implications in the basis. In addition to this,
        some bases have been defined to improve the computation of closures
        relying on the directness property. The efficiency of computation with
        the direct basis is achieved due to the fact that the closure is computed
        in one traversal.
        In this work, we focus on the D-basis, which is ordered-direct. An open
        problem is to obtain it from an arbitrary implicational system, so it is
        our aim in this paper. We introduce a method to compute the D-basis
        by means of minimal generators calculated using the Simplification Logic
        for implications.


 1    Introduction

 Discovering knowledge and information retrieval are currently active issues where
 Formal Concept Analysis (FCA) provides tools and methods for data analysis.
 The notions around the concept lattice may be considered as the main attractions
 in Formal Concept Analysis and they are strongly connected to the notion of
 closure.
     Closure system is a fundamental concept appearing in several areas such as
 database theory, formal concept analysis, artificial intelligence, etc. It is well-
 known that there exists a connection between a closure operator on a set and
 the lattice of its closed sets. Furthermore, the closure system can be presented,
 dually, as a set of attribute implications, namely an implicational system but
 this set has usually a lot of redundancy inducing non-desired properties.

c paper author(s), 2015. Published in Sadok Ben Yahia, Jan Konecny (Eds.): CLA
 2015, pp. 217–228, ISBN 978–2–9544948–0–7, Blaise Pascal University, LIMOS
 laboratory, Clermont-Ferrand, 2015. Copying permitted only for private and
 academic purposes.
218     Estrella Rodríguez-Lorenzo et al.


     We can not fail to mention the relevance of the role of the implication notion
in different areas. It was the main actor of the normalization theory in database
area; it has an outstanding character in Formal Concept Analysis and it was
prominently used in Frequent Set Mining and Learning Spaces, see the survey
of M. Wild [10]. The latter is devoted to mathematical theory of implications
and the different faces of the concept of an implication. Implications linked data
represented in several forms going from the relationship between itemsets in
transactions (Frequent Set Mining) to the boolean functions (Horn Theory).
     Nonetheless, as V. Duquenne says in [6] “it is surprising if not hard to ac-
knowledge that we did not learn much more on their intimacy in the meantime,
despite many interesting papers using or revisiting them”. We believe there is a
long way to go, and a deeper theory on properties of implications with automated
and efficient methods to manipulate them can be developed.
     In this paper, we are focused in the Formal Concept Analysis area and the
fundamental notions are assumed (see [7]). The task of information retrieval
carried out by the tools in FCA conduits to infer concepts from the data set,
i.e. to deduce (in an automated way) a set of objects that may be precisely
characterized by a set of attributes. Such concepts inherit an order relation
induced by attribute set inclusion, providing a lattice structure of the concept set.
Here implications are retrieved from a binary table (formal context) representing
the relationship between a set of objects and a set of attributes. Implications
represent an alternative way for the underlying information contained in the
formal context.
     Many applications must massively compute closures of sets of attributes and
any improvement of execution time is relevant. In [9] the author establishes the
necessity of obtaining succinct representation of closure operators to achieve an
efficient computational usage. In this direction, properties associated to implica-
tions are studied to render equivalent sets fulfilling desired properties, directness
and optimality.
     An important matter in FCA is to transform implicational systems in canon-
ical forms for special proposals in order to provide an efficient further man-
agement. Hence, some alternative definitions have been established: Duquenne-
Guigues basis, direct optimal basis, D-basis, etc. In this work we focus on the
last one [1], because it combines, in a balanced way, a brief representation (it
has a small number of elements) and a efficient computation of closures (it is
computed in just one traversal). To this end, D-basis proposes an order in which
implications will be attended.
     The major issue is that the execution of the D-basis in one iteration is more
efficient that the execution of a shorter, but un-ordered one, for instance the
canonical basis of Duquenne and Guigues. K. Adaricheva et.al prove in [1] that
one can extract the D-basis from any direct unit basis Σ in time polynomial
of size of Σ, and it takes only linear time of the number of implications of the
D-basis to put it into a proper order.
     In [5] we have proposed a method to calculate all the minimal generators from
a set of implications as a way to remove redundancy in the basis. The method
                    From an implicational system to its corresponding D-basis    219


to compute all the minimal generators is based on the Simplification Logic for
implications [8]. Using this logic we are able to remove redundancy in the impli-
cations [4] and following the same style of application of the Simplification Rule
to the set of implications we can obtain all the minimal generators.
    Currently the retrieval of the D-basis from an arbitrary implicational sys-
tem is an open problem, so it becomes our aim in this paper. We introduce a
method to compute the D-basis by means of minimal generators calculated us-
ing the Simplification Logic for implications. The relationship among minimal
generators, covers, minimal covers and D-basis is presented and an algorithm to
calculate D-basis from an arbitrary set of implications is proposed.
    Section 2 presents the main notions necessary to the understanding of the
new method: closure operators, the D-basis, Simplification Logic and the method
to calculate minimal generators. In Section 3, the relationships between covers
and generators are presented. In Section 4, the new method to obtain the D-
basis from a set of implications is shown, and some conclusions and extensions
are proposed in Section 5.


2      Background

2.1     Closure systems

Given a non-empty set M and the set1 2M of all its subsets, a closure operator
is a map φ : 2M → 2M that satisfies the following, for all X, Y ∈ 2M :
(1) increasing: X ⊆ φ(X);
(2) isotone: X ⊆ Y implies φ(X) ⊆ φ(Y );
(3) idempotent: φ(φ(X)) = φ(X).
We will refer to the pair hM, φi of a set M and a closure operator on it as a
closure system.
    In the next two subsections we will follow the introduction of the implica-
tional system based on the minimal proper covers 2 given in [1], which was named
there the D-basis.
    We will call closure system reduced, if φ({x}) = φ({y}) → x = y, for any
x, y ∈ M 3 . If the closure system hM, φi is not reduced, one can modify it to
produce an equivalent one that is reduced, see [1] for more details.
    We will now define a closure operator φ∗ , which is associated with a given
operator φ.
                  hM, φi be a closure system. Define φ∗ as a self-map on 2M
Definition 1. Let S
           ∗
such that φ (X) = x∈X φ(x), X ⊆ 2M .
      It is straightforward to verify that
1
  In the FCA framework, that set M can be thought a set of attributes of a context.
2
  Although in [1] it was introduced as minimal cover, here we name it minimal proper
  cover because in this paper we generalize the notion of cover in Section 3.
3
  To clarify the notation φ({x}) will be represented as φ(x) if no risk of confusion.
220       Estrella Rodríguez-Lorenzo et al.


Lemma 1. φ∗ is a closure operator on M .
      Given a closure system hM, φi, we introduce several important concepts.
Definition 2 ([1]). For x ∈ M we call a subset X ⊆ M a proper cover for x if
x ∈ φ(X) \ φ∗ (X). If X is a proper cover for x, it will be denoted as x ∼p X.

2.2     The D-basis
In this subsection, we briefly summarize the introduction of the D-basis in [1].
Its definition is strongly based on the notion of a minimal proper cover:
Definition 3. A proper cover Y for x is called minimal, if, for any other proper
cover Z for x, Z ⊆ φ∗ (Y ) implies Y ⊆ Z.
    The existence of several proper covers for the same element induces the need
to introduce the notion of minimality.
Lemma 2. If x ∼p X, then there exists Y such that x ∼p Y , Y ⊆ φ∗ (X) and
Y is a minimal proper cover for x. In other words, every proper cover can be
reduced to a minimal proper cover under the subset relation added with the φ∗
operator.
    These ideas bring to the following definition of the implicational system defin-
ing the reduced closure system by means of the minimal proper covers.
Definition 4. Given a reduced closure system hM, φi, define the D-basis ΣD
as a union of two subsets of implications:
1. {y → x : x ∈ φ(y) \ y, y ∈ M } (such implications are called binary);
2. {X → x : X is a minimal proper cover for x}.
    Note that the D-basis belongs to the family of the unit bases, i.e. implica-
tional sets where each implication A → b has a singleton b ∈ M as a consequent.
Lemma 3. ΣD generates hM, φi.

2.3     Ordered direct set of implications
Here we recall the notion of the ordered direct basis introduced in [1], which is
designed for a quick computation of the closures based on some fixed order of
implications. First we recall the definition of the ordered iteration of implications.
Definition 5. Suppose the set of implications Σ is equipped with some linear
order, or equivalently, the implications are indexed as Σ = {s1 , s2 , . . . , sn }. De-
fine a mapping ρΣ : 2M → 2M associated with this ordering as follows. For any
X ⊆ M , let X0 = X. If Xk is computed and implication sk+1 is A → B, then
                                  
                                    Xk ∪ B, if A ⊆ Xk ,
                          Xk+1 =
                                    Xk ,     otherwise.
Finally, ρΣ (X) = Xn . We will call ρΣ an ordered iteration of Σ.
                  From an implicational system to its corresponding D-basis      221


    The concept of the ordered iteration is central for the definition of the ordered
direct basis. For any given set of implications Σ on set M , by φΣ we understand
the closure operator on M defined by Σ. Equivalently, the fixed points of φΣ
are exactly subsets X ⊆ M which are stable for all implications A → B in Σ: if
A ⊆ X, then B ⊆ X.
Definition 6. The set of implications with some linear ordering on it, hΣ, <i, is
called an ordered direct basis, if, with respect to this ordering, φΣ (X) = ρΣ (X)
for all X ⊆ S.
    We note that any direct basis is ordered direct. By direct basis we understand
any set of implications that allows to produce the closure of subsets of the base
set while attending each implication only once.
    More precisely, if Σ is some set
                                  S of implications defining the closure system
hM, φi, then let πΣ (X) = X ∪ {B : A ⊆ X and (A → B) ∈ Σ}. In order
to obtain φ(X), for any X ⊆ M , one would normally need to repeat several
                                      2         3
iterations of πΣ : φ(X) = πΣ (X) ∪ πΣ   (X) ∪ πΣ  (X) . . . .
    The bases for which one can obtain the closure of any set X performing only
one iteration of πΣ , i.e., φ(X) = πΣ (X), are called direct. The various direct
bases appearing in the literature were surveyed in K. Bertet and B. Monjardet
[3]. Important result of their analysis is that in the family of all possible (unit)
direct bases for the same closure system, there exists a ⊆-smallest direct basis
Σcd , which was called as canonical unit direct basis.
    The following result from [1] summarizes the relation between the D-basis
ΣD and Σcd for a given closure system.
Theorem 1. ΣD ⊆ Σcd .

2.4   Minimal Generators via Simplification Logic
 In this subsection we summarize how the inference system of Simplification Logic
SLFD [4, 8] (equivalent to Armstrong’s axioms) is used to enumerate all minimal
generators. SLFD is guided by the idea of simplifying the set of implications by
efficiently removing some redundant attributes.
     SLFD logic considers reflexivity as axiom scheme
                                            A⊇B
                                    [Ref]
                                            A→B
and the following inference rules called fragmentation, composition and simpli-
fication respectively.
               A→B∪C                A → B, C → D                A → B, C → D
      [Frag]               [Comp]                     [Simp]
                A→B                 A∪C →B∪D                   A ∪ (C r B) → D
The important matter is that these rules can be considered as equivalence rules
which have been used as the core in automated methods for removing redun-
dancies, obtaining minimal keys, or computing closures. In particular, the last
method indicated is based on the following results (see [8] for details, proofs and
examples):
222     Estrella Rodríguez-Lorenzo et al.


Theorem 2 ([8]). Let A, B ⊆ M and Σ be a set of implications. We have
Σ ` A → B if and only if {∅ → A} ∪ Σ ` ∅ → B.

In order to compute closures, since φ(A) is the biggest subset B such that Σ `
A → B, the formula ∅ → A is used as a seed which guides the reasoning to
render the closure φ(A) just by applying the following equivalences where the
[Simp] inference rule plays a main role.

Proposition 1. Let A, B and C be subsets of M , then the following equivalences
hold:
 – Eq. I: If B ⊆ A then {∅ → A, B → C} ≡ {∅ → A ∪ C}.
 – Eq. II: If C ⊆ A then {∅ → A, B → C} ≡ {∅ → A}.
 – Eq. III: Otherwise {∅ → A, B → C} ≡ {∅ → A, B r A → C r A}.

Based on the above proposition and Theorem 2, in [5] the method Cls is in-
troduced. This method receives as input set A ⊆ M and a set of implications
Σ and renders the pair (φ(A), Σ 0 ) where Σ 0 is the set of implications simplified
with respect to ∅ → φ(A) (i.e. Σ contracted to 2M rφ(A) ).
Notation: From now on, in order to simplify the examples, elements of M are
denoted by numbers from 0 to 9 or lowercase letters and we omit brackets and
commas in subsets of M .

Example 1. Let Σ be {ab → c, ac → df, bcd → ef, f → c}. Then, Cls(af, Σ) =
(acdf, {b → e}) where φ(af ) = acdf and Σ 0 = {b → e} has important informa-
tion that will be used in the computation of minimal generators.

For X, Y ⊆ M satisfying that X = φ(Y ), it is usual to say that Y is a generator
of the closed set X. Notice that any subset of X containing Y is also a generator
of X. When the base set M of a closure system is finite, the set of generators of
a closed set can be characterized by its minimal ones.

Definition 7 (Minimal Generator). Let hM, φi be a closure system. X ⊆ M
is said to be a minimal generator if, for all proper subsets Y ⊂ X, one has
φ(Y ) ⊂ φ(X).

    In [5], a method to compute all minimal generators is presented. This method
named MinGen is based on above mentioned closure algorithm Cls and it takes
the advantages of the additional information that it provides. That is, Cls is used
not only to compute closed sets from generators but also a smaller implicational
set which guides us in the search of new subsets to be considered as minimal
generators. The input of MinGen is a subset of M and a set of implications Σ
and the output is the set of closed sets endowed with all the minimal generators
that generate them, i.e. {hC, mg(C)i : C is a closed set} where mg(C) is the set
of minimal generators D satisfying φ(D) = C. It can be seen as the lattice of
the closure system in which we have added labels with the minimal generators
to each closed set.
                  From an implicational system to its corresponding D-basis         223


Example 2. Let M = {a, b, c, d} and Σ = {a → b, c → bd, bd → ac}. Then the
MinGen algorithm returns the following set:
    MinGen(M, Σ) = {habcd, {c, ad, bd}i, hab, {a}i, hb, {b}i, hd, {d}i, h∅, {∅}i}
    Observe that there is trivial information (e.g. hb, {b}i) included in the output
of this algorithm. In [5], a version of MinGen, named MinGen0 , is also presented.
The difference between this algorithm and the previous one is that here only
non-trivial generators are calculated.
    For the same input, MinGen0 (M, Σ) = {habcd, {c, ad, bd}i, hab, {a}i, h∅, {∅}i}


3   Covers and Generators: Relationships
The definition of a D-basis is strongly based on the notion of a proper minimal
cover, and the latter is connected with the notion of a minimal generator. In
this paper we are going to work with covers instead of proper covers because
our idea is to manage, in a uniform way, the binary implications and non-binary
ones. Throughout this section we will assume that M is the base set of a closure
system defined either by closure operator φ, or by means of a set of implications
Σ and its related operator φΣ . The following definition introduces a notion that
extends the concept of a proper cover.

Definition 8 (Cover). Given X ⊆ M and x ∈ M , we say X is a cover of x,
denoted x ∼ X, if x ∈ φ(X) \ X.

The following proposition, whose proof is straightforward from definitions, illus-
trates the relationship among proper covers, covers and minimal generators.
Proposition 2. Let Σ be a set of implications. For each set C ⊆ M closed with
respect to φΣ and each X C,
1. X is a generator of C if and only if X is a cover of any x ∈ C r X.
2. If X is a proper cover of x ∈ M then X is also a cover of x.
Observe that the converse of the second item is not true, e.g. ad is a cover of
b (b ∼ ad) in Example 2 but it is not a proper cover. As we have remarked
in Section 2, the above definition of a cover does not coincide with the one
introduced in [1].
Corollary 1. Let Σ be a set of implications. For each set C ⊆ M closed with
respect to φΣ and each X   C, if X is a minimal generator of C then X is a
cover of any x ∈ C r X.
The following example illustrates these relationships and gives a counterexample
to the converse statement in the previous corollary.
Example 3. Let M = {a, b, c, d, e} and Σ = {a → d, bce → ad, bde → ac, ade →
bc, cd → abe, abd → ce}. In Table 1, the covers of each element of M and the non-
trivial minimal generators of each φΣ -closed set are shown. Moreover, underlined
covers are not proper covers whereas the others are.
224      Estrella Rodríguez-Lorenzo et al.



      Attribute Covers
         a     cd, bcd, bce, bde, cde, bcde            Closed Sets Minimal Generators
         b     ac, ae, cd, acd, ace, ade, cde, acde
                                                          abcde    ab, ac, ae, bce, bde, cd
         c     ab, ae, abd, abe, ade, bde, abde
                                                           ad      a
         d     a, ab, ac, ae, abc, abe, ace, bce, abce
         e     ab, ac, cd, abc, abd, acd, bcd, abcd

               Table 1. Covers, Proper Covers and Minimal Generators.



   Observe that, by Proposition 2, X is a cover of an element x if and only if
there exist a closed set C and a minimal generator Y of C such that Y ⊆ X and
x ∈ C r X. For example, cd is a minimal generator of abcde and cd ⊆ cde, thus,
cde is a cover of a but not a minimal generator.
The closure operator φ∗ allows us to introduce the notion of a minimal cover.
It will be used later to avoid redundancies in the implications of the basis when
we look for an optimal basis.

Definition 9 (Minimal Cover). Let Σ be a set of implications. Given X ⊆ M
and x ∈ M , X is a minimal cover of x if X is a cover and satisfies one of the
following conditions:
1. X is a singleton, i.e., | X |= 1.
2. for every cover Y of x, Y ⊆ φ∗ (X) implies X ⊆ Y .

From this definition we directly obtain the following proposition that relates
minimal generators and minimal covers.
Proposition 3. Let Σ be a set of implications, X ⊆ M and x ∈ M . If X is a
minimal cover of x ∈ φ(X) \ X, then X is a minimal generator of φ(X).
The following example illustrates the definition of minimal covers and shows
that the converse statement to the above proposition is not true.
Example 4. Given the base set and the set of implications of Example 3, Table 2
shows the minimal covers of each element.


                                 Element Minimal Covers
                                     a     cd, bce, bde
                                     b     ae, cd
                                     c     ab, ae, bde
                                     d     a, bce
                                     e     ab, cd
                                Table 2. Minimal Covers.
   Although ae is a minimal generator of abcde, it is not a minimal cover for all
x ∈ φ({a, b, c, d, e}) \ {a, e} = {b, c, d} but only for b and c.
                   From an implicational system to its corresponding D-basis     225


As the previous examples show, a minimal cover is always a minimal generator,
but not conversely. Thus, if we compute all minimal generators for some x ∈ M ,
we have, at the same time, all minimal covers for x, and the latter are exactly
what we need for the computation of the D-basis.


4      D-basis by means of Minimal Generators
We remark that it is an open problem to design an algorithm rendering the
D-basis from an arbitrary implicational system. In this section, we are going to
introduce such an algorithm based on the strong connection between minimal
covers and minimal generators. This is shown in Algorithm 1. To begin with, we
define the Function MinimalCovers to make the algorithm easier to understand.
    The input of the Function MinimalCovers is a set of covers of a given x ∈ M
and it returns a subset with all its minimal covers.


    Function MinimalCovers(L)
     input : A set of covers L
     output: The set of minimal covers
     begin
        foreach g ∈ L do
           foreach h ∈ L \ g do
               if h ⊆ g then
                   remove g from L
                                           [
               else if | g |6= 1 and h ⊆         φ(x) then
                                           x∈g
                   remove g from L

        return L


                                                               [
     Notice that although the condition h ⊆ g implies h ⊆           φ(x) and both of
                                                              x∈g
them lead to the same action, it is more efficient to split them off because the
cost of the first one is lower. Thus, we previously check the first one and, if it is
not fulfilled, then the other one is tested.
    As we have mentioned before, our goal is to obtain the D-basis from an
arbitrary implicational system. In the first stage, the algorithm computes all
minimal generators by means of the MinGen0 method proposed in [5]. Then, in
a second stage, it associates each minimal generator with all elements for which
it is a cover. In this stage, we have calculated a subset of covers containing
all minimal covers of a given attribute a. Finally, the algorithm removes those
intermediate covers which are not minimal covers, obtaining the D-basis. This
sequence of tasks is illustrated in Figure 1.
    As Figure 1 depicts, the transition from stage 1 to stage 2 needs a way to
associate the minimal generators with some of the elements in its closed set.
226       Estrella Rodríguez-Lorenzo et al.


                                                                  a ~Yk                   Yk minimal cover


            Original Situation                Stage 1                         Stage 2                            Stage 3
                    ....                         ....                           ....                               ....
                 Ak⟶Bk                    <Xk , Y1...Yn>                  a , ...Yk...                       a , ...Yk...
                   ....                         ....
                                                                                 ....                               ....

           Implicational System    Set of (non trivial) Closed sets       Set of Covers                  Set of Minimal Covers
                                     and Minimal Generators




                                  Fig. 1. Stages of D-basis algorithm



Thus, let a be an attribute and mg be the set of minimal generators such that
its closure contains a. We write this association as a pair ha, mgi. Let Φ be a
set of such pairs of attributes with their generators. We define the Function Add
which builds the set of covers produced in Stage 2 as follows:
          Add(ha, mgi, Φ) = {ha, {g ∈ mg|a 6∈ g} ∪ {mga }i : ha, mga i ∈ Φ}
   Then, in stage 3, the algorithm picks up the set of minimal covers from the set
obtained in stage 2 using the Function MinimalCovers. The method ends with
the Function OrderedComp which applies Composition Rule at the same time
that it orders the implications in the following sense: the first implications in the
D-basis are the binary ones (those with the left-hand side being a singleton).


 Algorithm 1: D-basis
      input : An implicational system Σ on M
      output: The D-basis ΣD on M
      begin
         MinGen:=MinGen0 (M , Σ)
         C:= ∅
         foreach hC, mg(C)i ∈MinGen do
            foreach a ∈ C do
                C:=Add(ha, mg(C)i, C)
         ΣD := ∅
         foreach ha, mga i ∈ C do
            mga :=MinimalCovers(mga )
            foreach g ∈ mga do ΣD := ΣD ∪ {g → a} ;
         OrderedComp(ΣD )
         return ΣD




Example 5. Algorithm 1 returns the following D-basis from the input implica-
tional system of Example 3:
          ΣD = {a → d, bce → ad, ab → ce, ae → bc, bde → ac, cd → abe}
                        From an implicational system to its corresponding D-basis                                                               227


   We emphasize that although ac is a minimal generator, it is not a minimal
cover, thus an implication with ac in the left-hand side is redundant (deduced
from inference axioms) and hence should not appear in the D-basis.


A detailed illustrative example

In the conclusion of this section we show the execution of the method, in all its
stages, on a set of implications from [3], which was used later to illustrate the
D-basis definition in [1].

Σ = {5 → 4, 23 → 4, 24 → 3, 34 → 2, 14 → 235, 25 → 134, 35 → 124, 15 → 24, 123 → 45}



    As a first step in the algorithm, MinGen0 renders the following set of pairs of
closed sets and its non-trivial minimal generators, see Figure 2:
        {h12345, {123, 14, 15, 25, 35}i, h234, {23, 24, 34}i, h45, {5}i, h∅, ∅i}


                              5⟶4, 2 3⟶4, 2 4⟶3, 3 4⟶2, 1 4⟶2 3 5, 2 5⟶1 3 4, 3 5⟶1 2 4, 1 5⟶2 4, 1 2 3⟶4 5




                        ø⟶5                                  ø⟶2 4                   ø⟶1 4                   ø⟶3 5             ø⟶1 2 3
                    4 5                                     2 3 4               1 2 3 4 5                   1 2 3 4 5         1 2 3 4 5
                   1⟶2 3, 2⟶1 3, 3⟶1 2                     1⟶5, 5⟶1                 ø                           ø                 ø
                                                                                        {1 4}                      {3 5}              {1 2 3}
                                          {5}                        {2 4}



                                                           ø⟶1       ø⟶5                        ø⟶2 5                      ø⟶1 5
                                                           1 5       1 5                      1 2 3 4 5                1 2 3 4 5
                  ø⟶1         ø⟶2       ø⟶3                 ø         ø                           ø                        ø
                                                           {1 2 4}   {2 4 5}
                1 2 3         1 2 3                                                                 {2 5}
                                        1 2 3                                                                                 {1 5}
                  ø             ø         ø
                 {1 5}          {2 5}      {3 5}

                                                   ø⟶2 3                     ø⟶3 4
                                                   2 3 4                     2 3 4
                                                  1⟶5, 5⟶1               1⟶5, 5⟶1
                                                            {2 3}                     {3 4}



                                        ø⟶1         ø⟶5                  ø⟶1          ø⟶5
                                        1 5         1 5                  1 5          1 5
                                         ø           ø                    ø            ø
                                        {1 2 3}     {2 3 5}              {1 3 4}      {3 4 5}




                                              Fig. 2. MinGen0 Execution


    Then, for each closed set and each of its elements, our algorithm renders the
following set of pairs of elements and covers:

              {h1, {25, 35}i, h2, {14, 15, 35, 34}i, h3, {14, 15, 25, 24}i,
                     h4, {123, 15, 25, 35, 5, 23}i, h5, {123, 14}i}

For each element, the Function MinimalCovers picks up its minimal covers:
       {h1, {25, 35}i, h2, {14, 34}i, h3, {14, 24}i, h4, {5, 23}i, h5, {14, 123}i}
   Finally, at the last step, the algorithm turns these pairs into implications and
applies ordered composition resulting in the D-basis.
  ΣD = {5 → 4, 23 → 4, 24 → 3, 34 → 2, 14 → 235, 25 → 1, 35 → 1, 123 → 5}
228     Estrella Rodríguez-Lorenzo et al.


5     Conclusion and future works
In this work we have presented a way to obtain the D-basis from any implica-
tional system. In [1] the algorithm was proposed to compute the D-basis from
any direct basis, but the computation from any implicational system was left
open. There exists also an efficient algorithm for the computation of the D-basis
from the context using the method of finding the minimal transversals of the
associated hypergraphs [2], but this assumes the different input for the closure
system which is outside the scope of this paper.
    The Function MinimalCovers renders the D-basis within the framework of
the closure systems without the need of any transformation. A key point of our
work is the connection between covers and generators. Using minimal gener-
ators, the D-basis is obtained by reducing the set of minimal generators and
transforming it into a set of minimal covers.
    As future work, we propose to develop an algorithm which computes the
D-basis with better integration of the minimal generator computation to render
the minimal covers in a more direct way. In addition, we are planning to design
an empirical study and to make a comparison between this algorithm and other
techniques proposed in previous papers.


Acknowledgment
Supported by Grants TIN2011-28084 and TIN2014-59471-P of the Science and
Innovation Ministry of Spain.

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