=Paper= {{Paper |id=Vol-1624/paper23 |storemode=property |title=Exploring Temporal Data Using Relational Concept Analysis: An Application to Hydroecological Data |pdfUrl=https://ceur-ws.org/Vol-1624/paper23.pdf |volume=Vol-1624 |authors=Cristina Nica,Agnès Braud,Xavier Dolques,Marianne Huchard,Florence Le Ber |dblpUrl=https://dblp.org/rec/conf/cla/NicaBDHB16 }} ==Exploring Temporal Data Using Relational Concept Analysis: An Application to Hydroecological Data== https://ceur-ws.org/Vol-1624/paper23.pdf

Exploring Temporal Data Using Relational
Concept Analysis: An Application to
Hydroecology

Cristina Nica1 , Agnès Braud1 , Xavier Dolques1
Marianne Huchard2 , Florence Le Ber1
1
ICube, University of Strasbourg, CNRS, ENGEES
firstname.lastname@engees.unistra.fr,agnes.braud@unistra.fr
http://icube-sdc.unistra.fr
2
LIRMM, University of Montpellier, CNRS
huchard@lirmm.fr
https://www.lirmm.fr

Abstract. This paper presents an approach for mining temporal data,
based on Relational Concept Analysis (RCA), that has been developed
for a real world application. Our data are sequential samples of biologi-
cal and physico-chemical parameters taken from watercourses. Our aim
is to reveal meaningful relations between the two types of parameters.
To this end, we propose a comprehensive temporal data mining process
starting by using RCA on an ad hoc temporal data model. The results
of RCA are converted into closed partially ordered patterns to provide
experts with a synthetic representation of the information contained in
the lattice family. Patterns can also be filtered with various measures, ex-
ploiting the notion of temporal objects. The process is assessed through
some quantitative statistics and qualitative interpretations resulting from
experiments carried out on hydroecological datasets.

1 Introduction
Exploring temporal datasets is a major challenge in current research and various
methods have therefore been proposed since the 90’s [1]. It is worth pointing out
that temporal data are relational, so that relational methods [6] can be useful
to respect their relational structure, e.g. [9]. In particular, Relational Concept
Analysis (RCA, [16]) allows to classify relational data and provides hierarchical
results which facilitates the analysis step.
Based on these properties, we propose to use RCA for exploring sequential
datasets from the hydroecological domain. These datasets were collected during
the Fresqueau project3 that focused on methods for assessing the quality of wa-
tercourses. The collected data represent biological (Bio) and physico-chemical
(PhC) samples taken at fixed points (river sites) and repeated in time. Both
parameters are used by the experts to determine the quality of watercourses.
3
http://engees-fresqueau.unistra.fr/presentation.php?lang=en
Therefore, a global assessment of the temporal relationship between PhC and
Bio parameters is needed. To this end, preprocessings of the raw sequential data
allow to build a qualitative temporal model that can be used to apply RCA on
these data. The RCA result is a family of lattices that can be navigated by the
users. The users can select relevant navigation paths through the lattices (start-
ing from concepts in a main lattice) by applying measures of interest based on
the concept extents, that can be linked to geographical information in our appli-
cation. Furthermore, in order to help their analysis and to synthetize the results,
we propose to transform those concepts within closed partially ordered patterns
(cpo-patterns, [5]), i.e. directed acyclic graphs where vertices are labelled with
information extracted from the concepts out of the family of lattices. Since con-
cepts can be more or less general or specific, the extracted patterns can be clas-
sified within three types, according to the number of vertices that are labelled
with general information. Then the users can choose to select and to navigate
general or specific paths in the lattices.
The paper is structured as follows. Section 2 presents basic definitions and
related work. Section 3 describes the hydroecological data and their preprocess-
ing while the RCA process is detailed in Section 4. Section 5 introduces some
measures of interest dealing with the temporal dimension of obtained concepts.
Section 6 presents cpo-patterns in order to help the analysis. Section 7 describes
and discusses the experimental results carried out on Fresqueau datasets. Section
8 concludes and gives a few perspectives of this work.

2 Basics and Related Work

Relational Concept Analysis (RCA, [16]) extends Formal Concept Analysis (FCA
[11]) to classify sets of objects described by attributes and relations, thus allowing
to discover knowledge patterns and implication rules in relational datasets. RCA
applies iteratively FCA on a Relational Context Family (RCF) that is consti-
tuted of a set K of object-attribute contexts and a set R of object-object contexts.
K contains n object-attribute formal contexts Ki = (Gi , Mi , Ii ) , i ∈ {1, ..., n}.
R contains m object-object relational contexts Rj = (Gk , Gl , rj ) , j ∈ {1, ..., m},
where Gk , called the domain of the relation, and Gl , called the range of the re-
lation, are respectively the sets of objects of Kk and Kl , and rj ⊆ Gk × Gl , k, l ∈
{1, ..., n}. At each step, object-attribute contexts are extended with relational
attributes taking the syntactic form qrj (C), where q is a quantifier, rj is a re-
lation and C = (X, Y ) is a concept where X is a subset of objects from the
range of rj . This paper uses the existential quantifier: ∃rj (C) is an attribute of
o ∈ Gk if rj (o) ∩ X 6= ∅. RCA process consists in applying FCA first on each
object-attribute context of an RCF, and then iteratively on each object-attribute
context extended by the relational attributes created using the concepts from
the previous step. The RCA result is obtained when the family of lattices of two
consecutive steps are isomorphic and the contexts are unchanged.
RCA has been applied to various data, e.g. for software model analysis and
re-engineering [2]. To our knowledge, this is the first time that RCA is used to
explore sequential datasets. There are, however, various related FCA approaches.
[18] introduced Temporal Concept Analysis where objects are characterized with
a date and a state (i.e. a set of attributes). Data are merged into a single context,
and the resulting concept lattice is analysed thanks to the date element in the
concepts, so that temporal relations between concepts are actually revealed by
the analyst. This approach has been used to analyse sequential data about crime
suspects [15]. In our RCA approach, the temporal relation between dates is
considered as an object-object relation and it links concepts from several lattices.
In [8], sequential datasets are processed without involving any partial order. In
[5], closed subsequences are mined and then grouped in a lattice similar to a
concept lattice. In [4], sequential data are mapped onto pattern structures whose
projections are used to build a pattern concept lattice. The authors combine the
stability of concepts and the projections of pattern structures in order to select
relevant patterns.
Besides, there exist various methods to explore qualitative sequential data.
Indeed, sequential pattern mining is an active research area, in relation to the
exponential growth of temporal and spatio-temporal databases. Sequential pat-
terns have been introduced by [1] and used for different purposes. Such an ap-
proach has been developed within the Fresqueau project and focused on closed
po-patterns, which were selected through various measures [7]. Indeed, selecting
relevant results is a main challenge for all approaches dealing with large datasets.
In FCA, the most used measures for selecting relevant concepts are stability [13],
probability and separation [12]. Unfortunately, these measures are not able to
take into account the specific structure of concepts built on temporal objects.
We thus propose to use specific measures, as detailed in Section 5.

3 Context and Data Preprocessing

In the Fresqueau project, the analysed data cover various compartments such as
physico-chemistry, hydrobiology, hydromorphology and land use (as described
in [3]). Here, we try to tackle the following issue by means of RCA: Can experts
explain values of biological parameters from PhC values occuring in past months
and thus improve the global assessment of the quality of watercourse ecosystems?
To answer this question we should mention that the quality of watercourses
is determined by the Bio parameters (e.g. Standardised Global Biological In-
dex (IBGN), Biological Index of Diatoms (IBD) and Fish Biotic Index (IPR)).
Hence, the objects of interest from our work are the Bio samples and we want to
assess, over a period of time, the impact of PhC macro-parameters (e.g. Nitrogen
(AZOT), Phosphor (PHOS) and Particulate Matter (PAES)) on Bio ones.
Table 1(a) illustrates a small raw sequential dataset of Bio and PhC samples
taken from a site (e.g. S1) corresponding to a river segment. A set of sites
constitutes a geographical area. A data sequence is a chronologically ordered set
of PhC samples with a Bio one at the end, all taken from the same site. This
raw sequential dataset shows measurements made only for IBGN Bio parameter
and for four PhC parameters namely Ammonium (N H4+ ), Kjeldahl Nitrogen
Table 1: Small example of raw and corresponding preprocessed sequential
dataset.

(a) Raw Sequential Data (b) Preprocessed Sequential Data
Site Date N H4+ N KJ N O2− P O43− IBGN Site Date AZOT PHOS IBGN
08/05 - - - - 10 08/05 - - Yellow
06/05 0.004 - 0.012 0.035 - 06/05 Blue Green -
S1
S1 09/04 - - - - 8 09/04 - - Orange
08/04 - 1.414 - - - 08/04 Green - -
01/04 0.043 0.146 0.421 - -

(N KJ), Nitrite (N O2− ) and Orthophosphate (P O43− ). For instance, 0.043 mg/l
of N H4+ is measured on 01/04, i.e. January 2004, for the site S1. An IBGN score
of 8/20 is measured on September 2004 for the same site.

The raw sequential dataset contains only numerical values. For mining such
data, we transform them by applying discretization and selection processes based
on domain knowledge. The discretization aims at converting numerical values
into qualitative ones. To this end, we use qualitative values for Bio and PhC
parameters that are provided by the SEQ-Eau4 standard. Both types of param-
eters have five qualitative values, namely very good, good, medium, bad and very
bad represented respectively by the colors blue, green, yellow, orange and red.
In addition, SEQ-Eau standard groups PhC parameters into macro-parameters.
For example, N H4+ , N KJ and N O2− are grouped into AZOT macro-parameter.
The selection process considers only relevant data by defining some constraints
based on expert advice. For instance, the only analysed PhC samples are those
taken within 4 months before a Bio parameter, from the same site.
Table 1(b) shows the preprocessed sequential dataset ready to be mined
using RCA. This sequential dataset is obtained by applying the discretization
and selection processes to the raw sequential dataset illustrated in Tab. 1(a).
It is worth pointing out that the preprocessed sequential dataset is significantly
small compared to the raw one thanks to the macro-parameters and the limited
analysed period of time.

4 Temporal Relational Analysis

The sequential dataset is structured following the schema depicted in Fig. 1. The
four rectangles represent the four sets of objects we manipulate: Bio samples,
PhC samples, Bio parameters and PhC parameters. The links between Bio/PhC
samples and PhC samples are defined by the temporal binary relation is preceded
by (denoted by ipb). This temporal relation associates one sample to another one
if the first sample is preceded in time by the second one, on the same site. There
4
http://rhin-meuse.eaufrance.fr/IMG/pdf/grilles-seq-eau-v2.pdf
Fig. 1: The modelling of the hydroecological sequential dataset.

is no temporal binary relation between Bio samples since in this work we evaluate
the impact of physico-chemistry on biology. The Bio/PhC samples are described
only by the qualitative relations has parameter blue/green/yellow/orange/red
that link the Bio/PhC samples with the measured Bio/PhC parameters. For
instance, has parameter green links the PhC samples taken from S1 on 08/04
(Tab. 1(b)) with AZOT PhC parameter.
Following the temporal data model illustrated in Fig. 1, we build the RCF de-
picted in Tab. 2 for a small hydroecological sequential dataset. The tables KPHC
(PhC parameters), KBIOS (Bio samples) and KPHCS (PhC samples) represent
object-attribute contexts. There is no object-attribute context for Bio parame-
ters because each dataset is restricted to one value of one parameter (here IBGN
red). KBIOS and KPHCS have no column since the samples are only described using
the qualitative relations. The tables RPHCS-ipb-PHCS, RBIOS-ipb-PHCS , RbPHC
and RgPHC represent object-object contexts. In these object-object contexts, a
row is an object from the domain of the relation, a column is an object from the
range of the relation and a cross indicates a link between two objects. For exam-
ple, RPHCS-ipb-PHCS defines the temporal relations (ipb) between PhC samples
and has KPHCS both as domain and range. RbPHC defines the qualitative relations
between PhC samples and PhC parameters that have the blue (b) qualitative
value.
Figure 2 represents the family of concept lattices obtained by applying RCA
on the RCF illustrated in Tab. 2. There are three lattices, one for each formal
context: LKPHCS (PhC samples, Fig. 2(a)), LKPHC (PhC parameters, Fig. 2(b)) and
LKBIOS (Bio samples, Fig. 2(c)). Each concept is represented by a box structured
from top to bottom as follows: concept name, simplified intent and simplified
extent. As said before, we have used the existential quantifier to build relational
attributes. For instance, the intent of C KPHCS 2 from concept LKPHCS contains the
relational attribute ∃RgPHC(C KPHC 1) inherited from concept C KPHCS 5. This
relational attribute is common to all PhC samples that measure a green PHOS
parameter, which represents the extent of concept C KPHC 1 shown in Fig. 2(b).
Table 2: RCF composed of object-attribute contexts: KPHC, KBIOS and KPHCS;
temporal object-object contexts: RBIOS-ipb-PHCS and RPHCS-ipb-PHCS; quali-
tative object-object contexts: RbPHC and RgPHC.
object-attribute contexts object-object contexts

S1 17/01
S1 10/01
S1 25/12
S2 28/02
S2 20/02

AZOT

AZOT
PHOS

PHOS
S1 17/01
S1 10/01
S1 25/12
S2 28/02
S2 20/02
KPHCS
AZOT
PHOS

KBIOS S1 17/01 RbPHC RgPHC
RPHCS-ipb-PHCS
S1 20/01 S1 10/01 RBIOS-ipb-PHCS S1 17/01 S1 17/01 × ×
KPHC S1 17/01 ×
S1 28/12 S1 25/12 S1 20/01 S1 10/01 × S1 10/01
AZOT × × × S1 10/01
S1 25/12 × S1 25/12 ×
PHOS × S2 30/02 S2 28/02 S1 28/12 × S1 25/12
S2 20/02 S2 30/02 S2 28/02 S2 28/02 × ×
× × S2 28/02 ×
S2 20/02 × S2 20/02 ×
S2 20/02

The navigation amongst the lattices shown in Fig. 2 follows the concepts
used to build relational attributes. For example, the aforementioned relational
attribute ∃RgPHC(C KPHC 1) allows us to navigate from concept C KPHCS 2 out
of LKPHCS to concept C KPHC 1 out of LKPHC .

5 Measures of Interest for Temporal Concepts
To analyse the results of the RCA process, experts start from a main lattice, here
the lattice LKBIOS , and navigate through the relational attributes linking concepts
of different lattices. Besides, since RCA process can produce a large number of
interrelated concepts, depending on the dataset volume and characteristics, some
interestingness measures are required to select relevant concepts from where to
start the navigation.
Such measures should take into account the specificity of concepts built on
temporal objects, whereas well-known measures (e.g. concept stability) fit basic
concepts. For example, Fig. 3 depicts two concept extents where the temporal
objects are the Bio samples. Both concepts – that we call temporal concepts
– have the same number of Bio samples and they cover the same geographical
area. If two Bio samples are deleted, following the idea of stability measure, one
of the site S2 and one of S3, then both concepts still have the same number of
Bio samples but they cover different river sites.
To overcome this limitation, we introduce below an approach based on the
distribution of temporal concept extents. The main idea in our method states
that a concept is relevant if it is frequent and related to many sites where Bio
samples are evenly distributed amongst these sites. Accordingly, we try to find
temporal concepts whose intents represent universally available regularities in
the studied geographical area. In our example, both concepts have the same fre-
quency (7 samples), but the distribution is different: Concept 1 is more relevant
than Concept 2.
Let (X, Y ) be a formal concept of the main lattice, then its extent X is a
set of temporal objects – or pairs – (Object, Date). If the value of Object is
not identical for all the pairs, then the pairs can be grouped into categories by
objects. We accordingly define X̄ which represents the set of distinct objects
C_KPHCS_0

C_KPHCS_5 C_KPHCS_4
∃RgPHC(C_KPHC_3) ∃RbPHC(C_KPHC_3)
∃RgPHC(C_KPHC_1) ∃RbPHC(C_KPHC_2) C_KBIOS_0
S1_10/01 ∃RBIOS-ipb-PHCS(C_KPHCS_0)
∃RBIOS-ipb-PHCS(C_KPHCS_5)
∃RBIOS-ipb-PHCS(C_KPHCS_4)

C_KPHCS_3
C_KPHCS_2
∃RgPHC(C_KPHC_2)
∃RPHCS-ipb-PHCS(C_KPHCS_0)
∃RPHCS-ipb-PHCS(C_KPHCS_4) S1_25/12 C_KBIOS_4 C_KBIOS_3
S2_20/02 ∃RBIOS-ipb-PHCS(C_KPHCS_3) ∃RBIOS-ipb-PHCS(C_KPHCS_2)
S1_17/01
S1_20/01 S1_28/12
C_KPHC_3

C_KPHCS_6
∃RPHCS-ipb-PHCS(C_KPHCS_5) C_KBIOS_2
∃RPHCS-ipb-PHCS(C_KPHCS_2) ∃RBIOS-ipb-PHCS(C_KPHCS_6)
C_KPHC_2 C_KPHC_1
S2_28/02 S2_30/02
AZOT PHOS
AZOT PHOS

C_KPHCS_1 C_KBIOS_1
C_KPHC_0
* ∃RBIOS-ipb-PHCS(C_KPHCS_1)

(a) LKPHCS (b) LKPHC (c) LKBIOS

Fig. 2: The family of concept lattices obtained by applying RCA on the RCF
given in Tab.2. The ∗ symbol represents all the relational attributes of KPHCS.

from X pairs: X̄ = {o ∈ O|∃t ∈ T, (o, t) ∈ X}, where O is the object set and T
the set of dates.

Definition 1 (Absolute Frequency (φo )). Let C = (X, Y ) be a temporal
concept and o an object of X̄. The absolute frequency of o in C, denoted φo , is
equal to the number of distinct pairs of X where o occurs. X̄φ = {(o, φo ) |o ∈ X̄}.

In our example (Fig. 3), X̄1 = X̄2 = {S1, S2, S3}. Concept 1 has X̄1φ =
{(S1, 3) , (S2, 3) , (S3, 1)} and Concept 2 has X̄2φ = {(S1, 5) , (S2, 1) , (S3, 1)}.

Fig. 3: Bio samples distribution by sites for two concept extents.
Definition 2 (Support and Richness (ρ)). The support of a concept (X, Y )
corresponds to the number of pairs (Object, Date) out of X. Its richness, repre-
sented by ρ, is defined as the cardinality of X̄.
Definition 3 (Distribution index (IQV)). The distribution of a concept
(X, Y ) describes the number of times each object out of X̄ occurs in X and it
is measured by the Index of Qualitative Variation (IQV, [10]). IQV is based on
the ratio of observed differences in X̄φ to the total number of possible differences
within X̄φ (ρ > 1).
ρ

2
P 2
ρ |X| − φo i
i=1
IQV = (1)
|X|2 (ρ − 1)
If ρ = 1, IQV = 0.
Our choice of IQV stems from the observation that the objects of X̄ do not
have an intrinsic ordering. Thus, measuring their distribution using the IQV
[10] seems interesting. The IQV ranges from 0 to 1. When all pairs of X contain
the same object, there is no diversity and the IQV is 0. In contrast, when there
are different objects and all pairs of X̄φ have equal φo , there is even distribution
and the IQV is 1.
Returning to our example (Fig. 3), both concepts have support |X1 | =
|X2 | = 7 and richness ρ1 = ρ2 = 3. For Concept 1 the distribution is IQV1 =
3[72 −(32 +32 +12 )]
72 (3−1) = 0.91 and for Concept 2 IQV2 = 0.67. Hence, Concept 1 is
computed as more relevant than Concept 2 since its objects (Bio samples) are
better distributed amongst the sites.

6 CPO-patterns for Helping Expert Analysis
Since our aim is to facilitate the analysis work, we propose, in addition to the
selection of relevant concepts, to convert those concepts into cpo-patterns. In-
deed cpo-patterns are structures with a graphical representation easy to read
and understand (e.g. Fig. 4). The expert can choose a cpo-pattern that high-
lights interesting, surprising knowledge, and deepen the analysis by exploring the
area in the lattice surrounding the corresponding concept. Thus, starting from
the family of lattices built using RCA, we extract cpo-patterns following the
approach proposed in [14]. It is worth pointing out that there is a cpo-pattern
for each concept out of the lattice corresponding to the objects of interest for
the study, i.e. LKBIOS in our work.
Formally, let I = {I1 , I2 , ..., Im } be a set of items. An itemset IS is a non
empty, unordered, set of items, IS = (Ij 1 ...Ij k ) where Ij i ∈ I. Let IS be the
set of all itemsets built from I. A sequence S is a non empty ordered list of
itemsets, S = hIS1 IS2 ...ISp i where ISj ∈ IS. The sequence S is a subsequence
of another sequence S 0 = hIS10 IS20 ...ISq0 i, denoted as S s S 0 , if p ≤ q and if
there are integers j1 < j2 < ... < jk < ... < jp such that IS1 ⊆ ISj0 1 , IS2 ⊆
ISj0 2 , ..., ISp ⊆ ISj0 p . Sequential patterns have been defined by [1] as frequent
Fig. 4: Hybrid cpo-pattern: each vertex corresponds to a set of parameter values,
edges represent the temporal relation, e.g. IBGNred is preceded by PHOSred that is
preceded by ?red ; this notation means that a PhC parameter with a red quality
has been measured.

subsequences found in a sequence database. A po-pattern is a directed acyclic
graph G = (V, E, l). V is the set of vertices, E is a set of directed edges such
that E ⊆ V × V, and l is a labelling function mapping each vertex to an itemset.
A partial order can be defined on G as follows: for all {u, v} ∈ V 2 , u < v if
there is a directed path from u to v. However, if there is no directed path from u
to v, these elements are not comparable. Each path of the graph is a sequential
pattern as defined before. The set of paths in G is denoted by PG . A po-pattern is
associated to the set of sequences SG that contains all paths of PG . Furthermore,
let G and G0 be two po-patterns with PG and PG0 their sets of paths. G is a
sub po-pattern of G0 , denoted by G g G0 , if ∀M ∈ PG , ∃M 0 ∈ PG 0
such that
0
M s M . A po-pattern G is closed, denoted cpo-pattern, if there exists no
po-pattern G0 such that G ≺g G0 with SG = SG0 .
As described in [14], thanks to the hierarchical structure of the RCA results,
more or less accurate cpo-patterns are extracted. Based on their accuracy, three
types of cpo-patterns could be defined: abstract, hybrid and concrete. Firstly,
the abstract cpo-pattern represents an imprecise common trend of the analysed
data. Secondly, the hybrid one, depicted in Fig. 4, corresponds to a more or less
accurate common trend of the analysed data. Finally, the concrete cpo-pattern
designates an accurate common trend of the analysed data.

7 Experiments and Discussion

The experiments are carried out on a MacBook Pro with a 2.9 GHz Intel Core i7,
8GB DDR3 RAM running OS X 10.9.5. RCA is applied using the RCAExplore5
tool. For the extraction and selection of cpo-patterns we have developed an algo-
rithm in Java 8 based on Java Collections Framework and Lambda Expressions.
Three sequential datasets (each dataset concerns only one Bio parameter
having the yellow quality) from the Fresqueau project are analysed: IBDyellow ,
IP Ryellow and IBGNyellow . These datasets are interesting since the yellow qual-
ity of watercourses represents a median area between good ecological status and
bad ecological status of watercourses. Other quality values have also been anal-
ysed but are not presented here. The objective is to extract more or less accurate
5
http://dolques.free.fr/rcaexplore
Table 3: The results of mining the Fresqueau datasets. Bio and PhC Samples
are the number of analysed samples; Output is the number of concepts from the
main lattice (LKBIOS ) and the lattice of PhC samples (LKPHCS ); CPO-patterns is
the number of the extracted cpo-patterns; Execution Time in seconds.

Datasets RCA Extraction Execution Time
Samples Output CPO-patterns
Index Quality RCA & Extraction
Bio PhC LKBIOS LKPHCS Concrete Abstract Hybrid
IPR 35699 39605 433 3388 31877 593
IBD yellow 80 194 32146 20947 503 1444 30198 115
IBGN 9414 11580 305 815 8293 32

cpo-patterns representing frequent PhC trends of watercourses common in many
sites. To this end, the datasets are preprocessed and temporally modelled as de-
scribed in Sections 3 and 4. The temporal relational analysis relies on the IceBerg
algorithm [17], which result is a concept lattice of frequent closed itemsets. A
10% threshold is used only for the input of Bio samples (it corresponds to the
lattice of Bio samples that covers the objects of interest from our work). The
choice of this value allows us to focus on the cpo-patterns that describe many
sites.
Table 3 shows some quantitative statistics regarding the temporal relational
analysis and the extraction of cpo-patterns. The results in Output column show
that the number of extracted concepts for the IBGN dataset is about 3 times
smaller than the number of extracted concepts for the IPR and IBD datasets.
This reveals greater heterogeneity in IPR and IBD datasets in contrast with
IBGN. Consequently, cpo-patterns linking PhC and IBGN Bio parameters repre-
sent more examples and will provide more reliable forecasts of the yellow quality
of watercourses.
The CPO-patterns columns represent the different types of extracted cpo-
patterns and illustrate their quite large number that has to be reduced. To this
end, we select relevant cpo-patterns based on the support, richness and distribu-
tion of the associated concepts (see Section 5). Figure 5 shows three scatter-plots
(for the three sets of extracted concrete cpo-patterns in Tab. 3) of the distribution
index (IQV) with respect to the support. The diameter of the circles is propor-
tional to the richness. The user can first explore a few selected cpo-patterns based
on high thresholds for these measures. Then he/she can follow the cpo-pattern
hierarchy to deepen the analysis, as described below, or select more cpo-patterns
based on lower thresholds. For example, by defining two thresholds θIQV = 0.98
and θSupport = 25, the top-6 (IBGN), the top-26 (IBD) and the top-30 (IPR)
best distributed and most frequent cpo-patterns are selected. Focusing on IBD,
if the thresholds are e.g. θIQV = 0.98 and θSupport = 20, 52 cpo-patterns are
selected. These cpo-patterns cover various numbers of sampling sites, and thus
more or less extensive geographical areas. To select greater or smaller areas, the
cpo-patterns are ranked by analysing the diameter of the circles.
The qualitative interpretation of the extracted cpo-patterns was performed
by an hydroecologist. In Fig. 6 is an interesting excerpt from the main lattice
of IBGNyellow dataset. This group of cpo-patterns is subsumed by the abstract
cpo-pattern of C KBIOS 868 (support = 28) that represents the less accurate
common trend: often before yellow IBGN are sampled simultaneously a green
PhC parameter and another yellow PhC parameter. Figure 6 also emphasizes
the well-known correspondence between MOOX (organic matter pollutions) quality
classes and IBGN ones: a yellow MOOX appears in the yellow IBGN cpo-pattern,
which is associated to C KBIOS 595. The concepts C KBIOS 720, C KBIOS 550
and C KBIOS 400 highlight the impact of phosphorus pollution (PHOS) on macro-
invertebrates (IBGN) that is a lesser-known fact.
Moreover, in Fig. 6 two benefits of exploring sequential data by means of RCA
are observed. The first one is the generalisation order regarding the structure
of the extracted cpo-patterns. For example, the structure of C KBIOS 400 cpo-
pattern is more specific than the structure of its ancestor cpo-patterns, i.e. there
exist a projection from its ancestor cpo-patterns into C KBIOS 400 cpo-pattern.
The second benefit is the generalisation of items. For instance, the C KBIOS 550
cpo-pattern reveals the rule {PAESgreen , PHOSyellow } → {IBGNyellow } that is a
specialisation of the rule revealed by the C KBIOS 720 cpo-pattern, that is {?green ,
PHOSyellow } → {IBGNyellow }. These properties are useful for the expert who can
navigate from specific to general patterns or vice versa.

8 Conclusion
We have introduced an original approach for exploring temporal data using
RCA. Given a hydroecological dataset, where data represent Bio or PhC samples
measured at a given time in a certain site, we find hierarchies of more or less
general cpo-patterns that summarize the impact of PhC parameters on Bio ones.
A comprehensive process for mining sequential datasets has been proposed: 1)
preprocessing of the raw data based on domain knowledge, 2) relational analysis
of the preprocessed data based on an original temporal data model, 3) selection of
temporal concepts using the distribution, the richness and the support measures,
and 4) extraction of cpo-patterns by navigating amongst temporal concepts (step

1,00 1,00 1,00

0,92 0,98 0,98
IQV

IQV

0,84 0,96 0,96

0,76 0,94 0,94

0,68 0,92 0,92
0 20 40 60 80 0 20 40 60 80 0 20 40 60 80
Support Support Support

(a) IBGN (b) IBD (c) IPR

Fig. 5: Concrete cpo-patterns by distribution index, support and richness of the
associated concepts.
Fig. 6: Excerpt from a hierarchy of cpo-patterns (IBGN yellow ).

detailed in [14]). Our method has been applied to sequential datasets from the
Fresqueau project.
The main benefits of our approach are as follows. Using RCA produces hier-
archical concepts, while cpo-patterns synthetize complex navigation paths, both
facilitating the expert analysis. Furthermore, the proposed measures on temporal
concepts are useful to select relevant information in our application.
In the future, we plan to apply our approach on other relational datasets.
This will require to deeply investigate the behaviour of our measures and maybe
to find other methods for selecting the extracted cpo-patterns.

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