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All content in this area was uploaded by Jean-François Viaud on Nov 20, 2016
Content may be subject to copyright.
Subdirect Decomposition of Contexts into
Subdirectly Irreducible Factors
Jean-Fran¸cois Viaud1, Karell Bertet1, Christophe Demko1, Rokia Missaoui2
1Laboratory L3i, University of La Rochelle, France
{jviaud, kbertet, cdemko}@univ-lr.fr
2University of Qu´ebec in Outaouais, Canada
rokia.missaoui@uqo.ca
Abstract. The size of a concept lattice may increase exponentially with
the size of the context. When the number of nodes is too large, it becomes
very difficult to generate and study such a concept lattice. A way to avoid
this problem is to break down the lattice into small parts. In the subdirect
decomposition, the small parts are factor lattices which are meaningful
in the Formal Concept Analysis (FCA) setting.
In this paper a walkthrough from a finite reduced context to its subdi-
rect decomposition into subdirectly irreducible subcontexts and factors
is given. The decomposition can be reached using three different points
of view, namely factor lattices, arrow relations and compatible subcon-
texts. The approach is mainly algebraic since it is based on abstract
lattice theory, except for the last point which is inherited from FCA. We
also propose a polynomial algorithm to generate the decomposition of
an initial context into subcontexts. Such a procedure can be extended to
conduct an interactive exploration and mining of large contexts, includ-
ing the generation of few concepts and their neighborhood.
Keywords: concept lattice, congruence relation, factor lattice, arrow
relation, arrow closed subcontext, compatible subcontext
1 Introduction
During the last decade, the computation capabilities have promoted Formal
Concept Analysis (FCA) with new methods based on concept lattices. Though
they are exponential in space/time in worst case, concept lattices of a reason-
able size enable an intuitive representation of data organized by a context that
links objects to attributes through a binary relation. Methods based on concept
lattices have been developed in various domains such as knowledge discovery
and representation, database management and information retrieval where some
relevant concepts, i.e. possible correspondences between objects and attributes
are considered either as classifiers, clusters or representative object/attribute
subsets.
With the increasing size of data, a set of methods have been proposed in
order to either generate a subset (rather than the whole set) of concepts and
their neighborhood (e.g. successors and predecessors) in an online and interac-
tive way [8, 20] or better display lattices using nested line diagrams [13]. Such
approaches become inefficient when contexts are huge. However, the main idea of
lattice/context decomposition into smaller ones is still relevant when the classifi-
cation property of the initial lattice is maintained. Many lattice decompositions
have been defined and studied, both from algebraic [6, 16] and FCA points of
view [13, 12]. We can cite Unique Factorisation Theorem [16], matrix decompo-
sition [2], Atlas decomposition [13], subtensorial decomposition [13], doubling
convex construction [5,17, 14, 3] and subdirect decomposition. The latter has
been widely studied many years ago, in the field of universal algebra [6,11],
and even in FCA [21–24,12]. To the best of our knowledge, there is no new
development or novel algorithms for subdirect decomposition of contexts.
In this paper we investigate the subdirect decomposition of a concept lattice
as a first step towards an interactive exploration and mining of large contexts.
The subdirect decomposition of a lattice Linto factor lattices (Li)i∈{1,...,n}, de-
noted by L →L1×· · · ×Ln, is defined by two properties (see important results in
[13]): (i) Lis a sublattice of the direct product L1× · · ·× Ln, and (ii) each projec-
tion of Lonto a factor is surjective. The first property establishes that each factor
lattice is the concept lattice of an arrow-closed subcontext, i.e. closed accord-
ing to the arrow relation between objects and attributes. This means that the
decomposition can be obtained by computing specific subcontexts. The second
property states that there is an equivalence between arrow-closed subcontexts
and congruence relations of L,i.e., an equivalence relation whose equivalence
classes form a lattice with elements closed by the meet and join operations. This
means that the concepts of Lcan be retrieved from the factor lattices, and the
classification property of Lis maintained since each equivalence relation forms
a partition of the elements. The last result establishes an equivalence between
arrow-closed subcontexts and compatible subcontexts, i.e. subcontexts such that
each concept corresponds to a concept of the initial lattice. This result gives a
way to compute the morphism from Linto the direct product, and thus to re-
trieve the concepts of Lfrom the factor lattices. In this paper, we deduce from
these results strong links between the following notions that have not been used
yet together as far as we know:
–Factors of a subdirect decomposition,
–Congruence relations,
–Arrow-closed subcontexts and
–Compatible subcontexts.
As suggested in [13], the contexts of the factors of a particular subdirect
decomposition, namely the subdirectly irreducible subcontexts, can be obtained
by a polynomial processing of each row/object of the initial context. Therefore,
the subdirect decomposition of a lattice can be extended to a subdirect decom-
position of its reduced context into subdirect and irreducible subcontexts.
In this paper, we propose a subdirect and polynomial decomposition of a
context into subcontexts by extending the subdirect decomposition of a lattice
into factor lattices. This decomposition leads to data storage saving of large
contexts. Indeed, the generation of the whole set of factor lattices can be avoided
by providing an interactive generation of a few (but not all) concepts and their
neighborhood from large contexts. Moreover, a focus on a specific factor lattice
can be proposed to the user by generating, partially or entirely, the concept
lattice and/or a basis of implications.
There are at least two reasons for studying this case of pattern manage-
ment. The first one comes from the fact that users tend to be overwhelmed
by the knowledge extracted from data, even when the input is relatively small.
The second reason is that the community of FCA has made progress in lattice
construction and exploration, and hence existing solutions can be adapted and
enriched to only target useful patterns (i.e. pieces of knowledge).
This paper is organized as follows. Section 2 introduces the subdirect de-
composition and the three different points of view, namely factor lattices, arrow
relations and compatible subcontexts. Section 3 contains the main contribution
of this paper about the subdirect decomposition and the proposed algorithms.
A preliminary empirical study is presented in Section 4 while Section 5 presents
future work.
2 Structural framework
Throughout this paper all sets (and thus lattices) are considered to be finite.
2.1 Lattices and Formal Concept Analysis
Algebraic lattice Let us first recall that a lattice (L, ≤) is an ordered set in
which every pair (x, y) of elements has a least upper bound, called join x∨y,
and a greatest lower bound, called meet x∧y. As we are only considering finite
structures, every subset A⊂Lhas a join and meet (e. g. finite lattices are
complete).
Concept or Galois Lattice A (formal) context (O, A, R) is defined by a set
Oof objects, a set Aof attributes, and a binary relation R⊂O×A, between O
and A. Two operators are derived:
–for each subset X⊂O, we define X0={m∈A, j R m ∀j∈X}and dually,
–for each subset Y⊂A, we define Y0={j∈O, j R m ∀m∈Y}.
A (formal) concept represents a maximal objects-attributes correspondence
by a pair (X, Y ) such that X0=Yand Y0=X. The sets Xand Yare respec-
tively called extent and intent of the concept. The set of concepts derived from
a context is ordered as follows:
(X1, Y1)≤(X2, Y2)⇐⇒ X1⊆X2⇐⇒ Y2⊆Y1
The whole set of formal concepts together with this order relation form a
complete lattice, called the concept lattice of the context (O, A, R).
Different formal contexts can provide isomorphic concept lattices, and there
exists a unique one, named the reduced context, defined by the two sets Oand
Aof the smallest size.
This particular context is introduced by means of special concepts or elements
of the lattice L, namely irreducible elements.
An element j∈Lis join-irreducible if it is not a least upper bound of
a subset not containing it. The set of join irreducible elements is noted JL.
Meet-irreducible elements are defined dually and their set is ML. As a direct
consequence, an element j∈Lis join-irreducible if and only if it has only one
immediate predecessor denoted j−. Dually, an element m∈Lis meet-irreducible
if and only if it has only one immediate successor denoted m+.
In Figure 1, join-irreducible nodes are labelled with a number and meet-ir-
reducible nodes are labelled with a letter.
Fig. 1. A lattice with its irreducible nodes
Fundamental Bijection A fundamental result [1] establishes that any lattice
(L, ≤) is isomorphic to the concept lattice of the context (JL, ML,≤), where JL
and MLare the join and meet irreducible concepts of L, respectively. Moreover,
this context is a reduced one.
As a direct consequence, there is a bijection between lattices and reduced con-
texts where objects of the context are associated with join-irreducible concepts
of the lattice, and attributes are associated with meet-irreducible concepts.
Figure 2 shows the reduced context of the lattice in Figure 1.
b c d f g j
2 x x x x x
3 x x x x
5 x x x
6 x x x
9 x x
Fig. 2. The reduced context of the lattice in Figure 1
2.2 Compatible and Arrow-closed Subcontexts
This section is dedicated to the equivalence between compatible and arrow-closed
subcontexts.
Compatible subcontexts Asubcontext of a formal context (O, A, R) is a triple
(J, M, R ∩J×M) such that J⊂Oand M⊂A. A subcontext (J, M, R ∩J×M)
of (O, A, R) is compatible if for each concept (H, N ) of (O, A, R), (J∩H, M ∩N)
is a concept of (J, M, R ∩J×M).
Arrow relations The arrow-closed subcontexts involved in the equivalence
are based on the arrow relations between join and meet irreducible concepts of
a lattice. Consider the reduced context (JL, ML,≤) of a lattice (L, ≤). Arrow
relations [4, 15] form a partition of the relation 6≤ (defined by not having x≤y)
by considering the immediate predecessor j−of a join-irreducible j, and the
unique immediate successor m+of a meet-irreducible m:
–jlmif j6≤ m,j≤m+and j−≤m.
–j↑mif j6≤ m,j≤m+and j−6≤ m.
–j↓mif j6≤ m,j6≤ m+and j−≤m.
–j◦mif j6≤ m,j6≤ m+and j−6≤ m.
In Figure 3, the reduced context of Figure 2 is enriched with the four relations
l,↑,↓, and ◦in the empty cells that both correspond to the case where j6≤ m:
b c d f g j
2××××× l
3× l × ↓ × ×
5× × × l l ◦
6× × l × ↓ ◦
9l × ◦ × ◦ ◦
Fig. 3. Arrow relation
As an illustration, let j= 5 and m=fbe join-irreducible and meet-
irreducible nodes respectively (see Figure 1). Then, j−= 2 and m+=c. The
relation 5 lfholds since 5 6≤ f, 5 ≤cand 2 ≤f.
Arrow-closed subcontext A subcontext (J, M, R ∩J×M) of a context
(O, A, R) is an arrow-closed subcontext when the following conditions are met:
–If j↑mand j∈Jthen m∈M
–If j↓mand m∈Mthen j∈J
As an example, the first subcontext of Figure 4 is an arrow-closed subcontext
of the reduced context of Figure 3 whereas the second one is not, due to the
down-arrow 6 ↓g.
c d f g
3 x x
5 x x
6 x x
c d f g
3 x x
5 x x
Fig. 4. Arrow-closed and non-arrow-closed subcontexts of the context in Figure 3
Equivalence theorem First let us introduce the first equivalence we need in
this paper, whose proof can be found in [13]:
Theorem 1. Let (J, M, R ∩J×M)be a subcontext of (O, A, R). The following
propositions are equivalent:
–The subcontext (J, M, R ∩J×M)is a compatible one.
–The subcontext (J, M, R ∩J×M)is an arrow-closed one.
2.3 Congruence Relations and Factor Lattices
In this section, we introduce the equivalence between congruence relations and
arrow-closed subcontexts.
Quotient An equivalence relation is a binary relation Rover a set Ewhich is
reflexive, symmetric, and transitive. An equivalence class of x∈Eis:
xR={y∈E|xRy}
The set of equivalence classes, called the quotient set E/R, is:
E/R ={xR|x∈E}
Factor lattice A congruence relation Θon a lattice Lis an equivalence relation
such that:
x1Θy1and x2Θy2=⇒x1∧x2Θy1∧y2and x1∨x2Θy1∨y2
The quotient L/Θ verifies the following statement:
xΘ≤yΘ⇐⇒ xΘ(x∧y)⇐⇒ (x∨y)Θy
With such an order, L/Θ is a lattice, called factor lattice. A standard theorem
from algebra, whose proof is omitted, states that:
Theorem 2. The projection L→L/Θ is a lattice morphism onto.
The second equivalence theorem We are now able to formulate the second
equivalence whose proof can be found in [13]:
Theorem 3. Given a lattice L, the set of congruence relations on Lcorresponds
bijectively with the set of arrow-closed subcontexts of the reduced context of L.
Congruence relations will be computed with this theorem. However, other
algorithms exist [9, 10].
2.4 Subdirect decompositions
In this section, we introduce the equivalence between subdirect decompositions
and sets of arrow-closed subcontexts.
Subdirect product
Definition 1. A subdirect product is a sublattice of a direct product L1× · · ·×Ln
of lattices Li, i ∈ {1, . . . , n}such that each projection onto a factor is surjective.
The lattices Li, i ∈ {1, . . . , n}are the factor lattices. A subdirect decomposition
of a lattice Lis an isomorphism between Land a subdirect product which can be
denoted as:
L →L1× · · · × LnLi
The third equivalence theorem The third and most important equivalence
whose proof can be found in [13], makes a connection with sets of arrows-closed
subcontexts when they cover the initial context:
Proposition 1. Given a reduced context (O, A, R), then the subdirect decom-
positions of its concept lattice Lcorrespond bijectively to the families of arrow-
closed subcontexts (Jj, Mj, R ∩Jj×Mj)with O=∪Jjand A=∪Mj.
3 Our contribution
3.1 Main Result
From the three previous equivalences found in [13], we deduce the following one:
Corollary 1. Given a lattice Land its reduced context (O, A, R), we have an
equivalence between:
1. The set of arrow-closed subcontexts of (O, A, R),
2. The set of compatible subcontexts of (O, A, R),
3. The set of congruence relations of Land their factor lattices.
Corollary 2. Given a lattice Land its reduced context (O, A, R), we have an
equivalence between:
1. The families of arrow-closed subcontexts of (O, A, R)covering Oand A,
2. The families of compatible subcontexts of (O, A, R)covering Oand A,
3. The families (θi)i∈Iof congruence relations of Lsuch that ∩i∈Iθi=∆with
x∆y ⇐⇒ x=y.
4. The set of subdirect decompositions of Land their factor lattices.
In the following, we exploit these four notions that, to the best of our knowl-
edge, have not been put together yet.
1. The subdirect decomposition ensures that Lis a sublattice of the factor
lattice product. Moreover, each projection from Lto a factor lattice is sur-
jective.
2. The congruence relations of Lindicate that factor lattices correspond to
their quotient lattices, and thus preserve partitions via equivalence classes.
3. The compatible subcontexts give a way to compute the morphism from L
onto its factors.
4. Arrow-closed subcontexts enable the computation of the reduced context of
the factor lattices.
In the following we present the generation of a particular subdirect decom-
position and show a possible usage of factor lattices.
3.2 Generation of Subdirectly Irreducible Factors
In this section, we consider subdirect decompositions of a lattice Lwith its re-
duced context (O, A, R) as input. From Corollary 2, a subdirect decomposition
of a lattice Lcan be obtained by computing a set of arrow-closed subcontexts of
(O, A, R) that have to cover Oand A. There are many sets of arrow-closed sub-
contexts and thus many subdirect decompositions. In particular, the decomposi-
tion from a lattice Linto Litself is a subdirect decomposition, corresponding to
the whole subcontext (O, A, R) which is clearly arrow-closed. A subdirect decom-
position algorithm has been proposed in [12]. However, all congruence relations
are computed and then only pairs of relations are formed to get a decomposi-
tion. As a consequence, potentially multiple decompositions are produced with
necessarily two factors.
In this article, we focus on the subdirect decomposition of a context into a
possibly large number of small factors, i.e. factors that cannot be subdirectly
decomposed. A factor lattice Lis subdirectly irreducible when any subdirect
decomposition of Lleads to Litself. A nice characterization of subdirectly irre-
ducible lattices can be found in [13]:
Proposition 2. A lattice Lis subdirectly irreducible if and only if its reduced
context is one-generated.
A context (O, A, R) is one-generated if it can be obtained by arrow-closing
a context with only one j∈J. Thus (O, A, R) is the smallest arrow-closed
subcontext containing j∈J.
Therefore, we deduce the following result:
Proposition 3. Let Lbe a lattice. From L, we can deduce a product lattice
L1×... ×Lnwhere each lattice Liis:
–the concept lattice of a one-generated subcontext,
–subdirectly irreducible,
–a factor lattice of the subdirectly irreducible decomposition.
From this result, we propose an algorithm (Algorithm 1) to compute in poly-
nomial time the contexts of the factor lattices L1, . . . , ...Lnof a subdirectly
irreducible decomposition, with a reduced context (O, A, R) as input. The one-
generated subcontexts for each j∈Jare obtained by arrow-closing (Algorithm
2). The subdirectly irreducible decomposition of Lcan then be obtained by
computing the concept lattices of these subcontexts.
One can notice that the closure computed from join-irreducible concepts can
also be calculated from meet-irreducible concepts.
Algorithm 1: Subdirect Decomposition
Input: A context (O, A, R)
Output: List Lof the contexts (Jj, Mj, Rj) of the subdirectly irreducible
factor lattices
1L←∅;
2forall the j∈Odo
3Compute (Jj, Mj, Rj) = Arrow Closure((j, ∅,∅),(O, A, R)), the
one-generated subcontext span by j;
4if Ldoes not contain any subcontext that covers (Jj, Mj, Rj)then
5add (Jj, Mj, Rj) to L
6if Lcontains a subcontext covered by (Jj, Mj, Rj)then
7delete it from L
8return L;
Algorithm 2: Arrow Closure
Input: A subcontext ( ˜
J, ˜
M, ˜
R) of a context (J, M, R)
Output: Arrow-closure of ( ˜
J, ˜
M, ˜
R)
1Jc=˜
J;Mc=˜
M;
2predJ= 0; predM= 0;
3while predM<card(Mc)or predJ<card(Jc)do
4predJ= card(Jc);
5predM= card(Mc);
6forall the j∈Jcdo
7add to Mcall m∈Msuch that j↑m;
8forall the m∈Mcdo
9add to Jcall j∈Jsuch that j↓m;
10 Return (Jc, Mc, R ∩Jc×Mc)
jArrow Closure Con-
tained
in L
Input Output
(˜
J, ˜
M, ˜
R)JcMc
2 (2,∅,∅){2} {j} ×
3 (3,∅,∅){3} {c}
5 (5,∅,∅){3,5,6} {c, d, f, g} ×
6 (6,∅,∅){6} {d}
9 (9,∅,∅){9} {b} ×
Fig. 5. Iterations of Algorithm 1 for the reduced context in Figure 2
Consider the reduced context in Figure 2. Each iteration of Algorithm 1 is
described by Figure 5 for each value of j, the input and output of Algorithm
2, and the three one-generated subcontexts that belong to Lat the end of the
process. Therefore we get three factor lattices (see Figure 6).
The first subcontext is the one on the left of Figure 4. The two other ones
are: ({2},{j},∅) and ({9},{b},∅). The latter two subcontexts are interesting
because:
–They show that the initial lattice has parts which are distributive. Indeed,
these two subcontexts contain exactly one double arrow in each line and each
column.
–They give us a dichotomy: any concept contains either 2 or j; any concept
contains either 9 or b
–In the reduced context, an arrow brings a deeper knowledge than a cross.
(a)
({2},{j},∅)
(b) First subcontext in
Figure 4
(c)
({9},{b},∅)
Fig. 6. The three factor lattices of the decompostion with their subcontext as caption
The context on the left hand-side of Figure 4 is tricky to understand. For
the other ones, we have a simple relation 2 ljor 9 lb, which means that, for
instance, 2 and jare some kind of complement or converse.
Figure 7 shows a factor lattice and its corresponding congruence.
Fig. 7. Factor lattice and congruence
3.3 Onto Morphism and FCA
A subdirect decomposition of a lattice Linto factor lattices L1, . . . , Lnis relevant
since there exists an into morphism from Lto the product lattice L1×. . . ×Ln.
This morphism is specified by the bijection between compatible subcontexts and
congruence relations stated by Corollary 1:
Proposition 4. Let (J, M, R ∩J×M)be a compatible subcontext, then the
relation ΘJ,M defined by:
(A1, B1)ΘJ,M (A2, B2)⇐⇒ A1∩J=A2∩J⇐⇒ B1∩M=B2∩M
is a congruence relation, and its factor lattice is isomorphic to the concept lattice
of the subcontext (J, M, R ∩J×M).
Algorithm 3 computes this morphism: each concept of Lis computed as the
product of concepts in each factor, and then marked in the product lattice.
From Algorithm 3, we get the large tagged lattice product shown in Figure 8.
Obviously, this algorithm is not intended to be used in a real application with
large contexts since the product lattice is much more bigger than the original
one, while the main goal of the decomposition is to get smaller lattices. We only
use this algorithm in the empirical study.
Nevertheless, this morphism can be extended to develop basic FCA pro-
cessing. Once the subdirectly irreducible decomposition of a reduced context
(O, A, R) into the contexts C1, . . . , Cnis computed, an interactive exploration
and mining process can easily be considered by using the following basic tasks
and avoiding the generation of the lattice for the whole context (O, A, R):
–Compute the smallest concept of Lthat contains a given subset of objects
or attributes, and identify its neighborhood
–Compute the smallest concept cij and its neighborhood in a subset of factors
that contain a given collection of objects or attributes. Each factor Liis a
specific view of data.
Algorithm 3: Into morphism
Input: Initial lattice L;
Subcontexts (Jj, Mj, Rj);
Product lattice P=L1×. . . ×Ln
Output: Product lattice Pwith nodes coming from Lmarked.
1forall the c= (A, B )∈Ldo
2forall the (Jj, Mj, Rj)do
3Compute (A∩Jj, B ∩Mj);
4Mark, in P, the product node Πj(A∩Jj, B ∩Mj);
4 Experiments
In this section, we conduct an empirical study in order to better understand the
impact of the subdirect decomposition on contexts with different density levels.
All the tests were done using the java-lattices library available at:
Fig. 8. Ugly tagged lattice product
http://thegalactic.github.io/java-lattices/
A thousand of contexts of 10 observations and 5 attributes were randomly
generated. The experiments have been done three times using three density
values, namely 20%, 50% and 80%. Figure 9 presents the number of generated
lattices according to their size and their density. We can observe two histograms
with a nice gaussian shape in the first two cases, but a strange behavior in
the last case. However, we roughly observe that the higher the density is, the
bigger the lattices are. Therefore, the context density will have an impact on the
decomposition process.
(a) Density of 20% (b) Density of 50% (c) Density of 80%
Fig. 9. Number of lattices per size.
The number of generated factors is given in Figure 10. One can observe
that this number increases with the density of the initial context since the corre-
sponding lattices have more edges. With a context density of 20%, we get 87.40%
irreducible contexts. However, with a density of 80%, only 1.50% of contexts are
irreducible and 70% of contexts have at least 4 factors in their decomposition.
Thus, lattices are more decomposable when they come from dense contexts.
Factors Density=20% Density=50% Density=80%
1 87.40% 70.50% 1.50%
2 9.70% 21.40% 9.90%
3 2.60% 6.20% 18.70%
4 0.30% 0.50% 23.40%
5 1.40% 28.50%
6 18.00%
Fig. 10. Proportion of the number of factors in the subdirect decomposition of the
contexts according to their density
Let us now examine the size of factors with two different density values,
namely 20% and 50%. Figure 11 gives the number of cases (initial lattices)
according to the number of produced factors. Of course, we observe that smaller
lattices give rise to smaller factors. We can also see that in these two density
cases, the largest number is obtained for factors of size 2.
(a) Density 20% (b) Density 50%
Fig. 11. Number of cases according to the number of generated factors.
The last part of the tests aims at using contexts with a large density of 80%
and computing the ratio between the number of nodes (edges resp.) of the initial
lattice and the number of nodes (edges resp.) of the product lattice.
We thus get Figure 12 which shows that the higher is the number of factors,
the bigger is the product lattice. Consequently, the set of useless (void) nodes in
the product becomes larger as the number of factors increases.
We have also conducted experiments with 40 observations and 15 attributes,
and a hundred of contexts were generated using two density values: 20% and
50%. Unfortunately, all were subdirectly irreducible.
Factors % of nodes % of edges
1 100% 100%
2 97.90% 97.20%
3 89.20% 84.60%
4 85.00% 77.40%
5 80.60% 71.80%
6 68.00% 57.30%
Fig. 12. Proportion of untagged nodes and edges
5 Conclusion and future work
In this paper, we have presented a polynomial algorithm for the decomposition
of a reduced context into subcontexts such that the concept lattice of each sub-
context is a subdirectly irreducible factor. This decomposition is a direct conse-
quence inferred from strong links between factors of a subdirect decomposition,
congruence relations, arrow-closed subcontexts and compatible subcontexts es-
tablished in [13].
To further investigate the subdirect decomposition, it would be interesting to
conduct large-scale experiments on real world data not only to confirm/nullify
the preliminary empirical tests but also to understand the semantics behind
the generated irreducible contexts. In particular, attributes covered by several
factors interfere in different views of data whose semantics must be interesting
to understand. Moreover, it would be useful to allow the user to interactively
select a few factors of the decomposition by mixing our approach with the one
in [12].
From a theoretical point of view, we think that there are strong links between
the implication basis in a quotient lattice and the one from the initial lattice. To
the best of our knowledge, this issue has never been addressed and could have
significant algorithmic impacts. However, we note that [19] tackle a similar issue
in case of a vertical decomposition of a context into subcontexts.
Since the empirical study in [18] show that many real-life contexts are subdi-
rectly irreducible, we plan to (i) identify cases in which a context is necessarily
irreducible, and (ii) study, compare and combine other decompositions, in par-
ticular the Fratini congruence [7], and the reverse doubling convex construction
[5, 17, 14, 3]. Finally, the construction of a lattice from its factor lattices can be
done based on the optimization principles behind the relational join operation
in databases.
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