Commit 39cb36cc authored by Vincent Mazenod's avatar Vincent Mazenod
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<h2><i class="fas fa-comment-dots"></i> Description</h2>
<p>
Multi-proxy palaeo- and ecological research usually provides large and heterogeneous databases with temporal relationships between
components. It is noteworthy that significant cross-correlations of different indicators and their repeated co-evolutions through
time are not easy to characterize empirically. MOBI-PALEO was created for this reason.
</p>
<p>
The overall objective here is to extract frequent closed gradual patterns or FCGP (Di-Jorio et al., 2008) that track the order
correlations of the form “the more/less X associated with the more/less Y…” from large databases with a task automation and thus a
reduced runtime. This automatic patterning work is based on a data-driven modelling, which confirms data mining methods are
complementary to multivariate statistics, which allow user-driven modelling of data. Algorithms of gradual patterns mining
currently reported in the literature do not assume any temporal constraints on data, yet numerical palaeoecological databases present
temporal relationships between objects (time-scaled data). The application of data mining methods in palaeoecology is to perform a data
mining process under temporal constraint. This need for a temporal dimension motivated our creation of a new and specific algorithm
allowing to automatically extract co-evolutions between paleoecological indicators. The basic principles and the methodology used to
obtain it are detailed in Lonlac et al. (2017 and 2018).
</p>
<p>
Briefly, the initial database in tabular form is a set of objects (the different depths or the equivalent estimated radiocarbon dates)
described by a set of attributes. This table displays the abundance (in percentages) of each attribute for each object.
In this database, a gradual item corresponds to (attribute 1=+), for instance, while {attribute 1=+, attribute 2=+, …},
for example, is a gradual pattern, which indicates that these 2 types of attributes are positively correlated (in term of covariation).
An algorithm, inspired from the approach proposed by Berzal et al. (2007), allows firstly to transform the original numerical
paleo- or ecological database in a categorical database. The APRIORI algorithm (Agrawal and Srikant, 1994) is secondly applied
on the obtained categorical database to extract frequent closed item sets corresponding to the frequent closed gradual patterns (FCGP)
of the original numerical database, which is constituted by objects temporally ordered. The obtained gradual patterns
are finally post-processed according to the user preferences and research objectives in order to reduce the number of patterns and
focus on the most interesting patterns.
</p>
<p>
FCGP correspond to the most concise representation of patterns without any loss of information (Pasquier et al., 1999). In this sense,
the FCGP with a low support of at least 10% and positively correlated have been retained. The support measures the redundancy of a FCGP
in the database and low support values ensure no loss of information. FCGP correspond to the most significant and repeated co-evolutions
of indicators.
Rather than characterizing specific correlations between individuals and variables,
the data mining approach allowed by Mobipaleo aims at performing a faster, systematic
and objective exploration of the meanings of raw, complex and heterogeneous databases
in order to highlight meaningful “hidden patterns or associations” (p. 4, Gilbert et al., 2018)
and to consider their modelling and their potential predictability. More precisely, the local
exploration of the databases allowed by the data mining approach aims at discriminating meaningful
key groups of indicators named Frequent Closed Gradual Patterns or FCGP.
</p>
<p>
Thus, the overall objective of Mobipaleo is to deploy task automation to rapidly extract frequent
closed gradual patterns (Di Jorio et al., 2008) that track the order correlations of the form
“the more/less taxon X associated with the more/less taxon Y…” from large databases.
This automatic patterning work is based on a data-driven modelling, which confirms data mining
methods are complementary to multivariate statistics, which allow user-driven modelling of data.
</p>
<p>
Algorithms of gradual pattern mining currently reported in the literature do not assume any temporal
constraints on data, yet all palaeoecological and ecological data contain temporal relationships
between objects (time-scaled data). The application of data mining methods in palaeoecology seeks
to perform a data mining process under temporal constraint. This need for a temporal dimension
motivated our creation of a new and specific algorithm allowing the automatic extraction of data
on co-evolutions between paleoecological or ecological indicators.
</p>
<p>
Briefly, the initial database in tabular form is a set of objects (the temporal component) described by
a set of attributes (multi-variate indicators). This table displays the abundance or amount (different units possible)
of each attribute for each object. In this database, a gradual item corresponds to (Indicator-type 1=+), for instance,
while {Indicator-type 1=+, Indicator-type 2=+, …}, for example, is a gradual pattern, which indicates that these 2
types are positively correlated (in term of covariation). The complexity of the data set is related to the order
associated to input objects. An algorithm, inspired by Berzal et al. (2007), first transforms the original numerical
palaeoecological database in a categorical database. More precisely, the original numerical palaeoecological database
D contains an attributes (variables) set with numerical data I = {i1, …, in} and an ordered set of objects (depths)
T = {t1,…, tm}, where t[i] gives the value of attribute I on the object t.
</p>
<p>
Our algorithm first built from the original numerical palaeoecological or ecological database D, a new database D’
containing a categorical attribute set I’ = {i1+, i1-, i1=,…, in+, in-, in=} such that:
For each attribute i of D, and for all couple of consecutive objects (tj, tj+1) of D,
<ul>
<li>if tj+1[i] > tj[i] a categorical attribute “i1+” is created to mean that the value of attribute i increases</li>
<li>if tj+1[i] < tj[i], a categorical attribute i1- is created to mean that the value of attribute i decreases.</li>
<li>if tj+1[i] = tj[i] a categorical attribute “i1=” is created to mean that the value of attribute i remains constant.</li>
</ul>
</p>
<p>
The previous procedure describes the first step of our gradual pattern mining algorithm, more precisely, how to obtain
categorical database from the initial numerical database.
</p>
<p>
In the second step of our algorithm, we apply a modified version of APRIORI (Agrawal & Srikant, 1994) algorithm to
the categorical database obtained at the previous step. APRIORI is a seminal algorithm for mining frequent itemsets.
We use a modified version of APRIORI to extract from categorical database obtained D’ the frequent closed itemsets,
which correspond to the frequent closed gradual patterns (FCGP) of the original numerical database D. The resulting
gradual patterns are finally post-processed in relation to the user’s scientific issues and research objectives in
order to identify patterns relevant to the research questions.
</p>
<p>
FCGP correspond to the most concise representation of patterns without any loss of information (Pasquier et al., 1999).
In this sense, the FCGP with a low support of at least 10% and positively correlated are retained. The support measures
the redundancy of a FCGP in the database and low support values ensure no loss of information. FCGP correspond to the
most significant and repeated co-evolutions of bioindicators.
</p>
<p>
Extract from “Supplementary Materials” (Miras et al., 2022).
</p>
<p>
For more details, please see Lonlac et al. (2018) and Miras et al. (2022). Full references in the tap
<a class="btn btn-primary" href="{{ path('publications') }}"><i class="fas fa-book-open"></i> publications</a>
</p>
<h3><i class="fas fa-asterisk"></i> References</h3>
<ul>
<li>
Agrawal R., Srikant R., 1994. Fast algorithms for mining association rules in large databases.
Proceedings of the 20th VLDB Conference, Santiago de Chile, Chile, September 12-15, 487-499.
</li>
<li>
Berzal F., Cubero J.C., Sánchez D., Miranda M.A.V., Serrano J., 2007. An alternative approach to discover gradual dependencies.
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, 5: 559-570.
</li>
<li>
Di-Jorio, L., Laurent, A., Teisseire, M., 2008. Fast extraction of gradual association rules: a heuristic based method.
CSTST, Cergy-Pontoise, France, October 28-31, 205–210.
</li>
<li>
Gibert K., Izquierdo J., Sànchez-Marrè M., Hamilton S.H., Rodríguez-Roda I., Homes G., 2018. Which method to use?
An assessment of data mining methods in Environmental Data Science. Environmental Modelling & Software, 110, p. 3-27.
</li>
<li>
Lonlac Konlac J., Miras Y., Beauger A., Peiry J.L., Mephu-Nguifo E., 2017. Une approche d’extraction des motifs graduels
fermés fréquents sous contrainte de la
</li>
</ul>
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......@@ -4,28 +4,37 @@
<div class="row">
<div class="col-9">
<!-- h2><i class="fas fa-cogs"></i> Mobipaleo</h2 -->
<h3>ecosystem and biodiversity dynamics from paleoecological and ecological studies</h3>
<h3>
Tracking biodiversity trajectories and modelling ecosystems dynamics through a data mining approach
</h3>
<p>
The online tool MOBI-PALEO is based on an innovative approach of time constrained data mining.
The overall objectives are:
Patterning past or recent ecosystems dynamics, climate fluctuations or biodiversity trajectories are
a key-issue to palaeoenvironmental and environmental researches, which provide large and heterogeneous
databases with temporal relationships between components. Data mining methods are appropriate for this
task and Mobipaleo aims to make data mining methods more accessible to a wider audience,
in particular in socio-environmental sciences.
</p>
<p>
The online tool MOBIPALEO is based on an innovative approach of time constrained data mining.
The overall objectives are to:
<ol>
<li>to obtain a faster and optimized cross-check of multi-variate indicators;</li>
<li>to identify ecological correlations of groups of key-proxy multi-variate indicators;</li>
<li>to extract time series from large data-sets with a reduced runtime.</li>
<li>accelerate and improve the cross-check of the relationships between different multi-variate indicators;</li>
<li>
identify ecological correlations of groups of key-proxies and specific multivariate bio-indicators
to explore their potential as early warning signals of tipping points in the ecosystems dynamics;
</li>
<li>rapidly extract temporal series from the large data set.</li>
</ol>
</p>
<p>
This tool is adapted to all kind of palaeoecological and ecological issues
(e.g., biodiversity trajectories, climate and ecosystems dynamics) and to all kind of time-scales
(from multi-millennia scales to short time scales).
Mobipaleo is adapted to all kind of socio-ecological issues and large databases, with temporal relationships between components.
</p>
<hr />
<p>
MOBI-PALEO has totally free access and is respectful of user data, ownership and privacy.
MOBIPALEO is open access (after a free registration) and respectful of user data, ownership and privacy.
</p>
<p>
To know how to use online tool MOBI-PALEO please read
To know how to use online tool MOBI-PALEO please read
<a class="btn btn-primary" href="{{ path('tutorial') }}">
<i class="fas fa-book"></i>
tutorial
......@@ -33,7 +42,7 @@
</p>
<p>
To use MOBI-PALEO, you must first <a class="btn btn-primary" href="{{ path('register') }}"><i class="fas fa-user-plus"></i> register</a>
before <a class="btn btn-primary" href="{{ path('login') }}"><i class="fas fa-sign-in-alt"></i> login</a> in.
before <a class="btn btn-primary" href="{{ path('login') }}"><i class="fas fa-sign-in-alt"></i> login</a>.
</p>
</div>
<div class="col-3">
......
......@@ -8,11 +8,7 @@
<tr>
<td>
Jerry Lonlac Konlac<br />
Post-doctorant
</td>
<td>
GEOLAB - LIMOS - Clermont-Ferrand<br />
now : Professeur associé au CERI SN, IMT Nord Europe
Professeur associé au CERI SN, IMT Nord Europe
</td>
<td>
<a href="mailto:jerry.lonlac@imt-nord-europe.fr">jerry.lonlac@imt-nord-europe.fr</a>
......@@ -21,41 +17,29 @@
<tr>
<td>
Yannick Miras<br />
Ingénieur de Recherche
</td>
<td>
GEOLAB - Clermont-Ferrand<br />
désormais : HNPN-Paris
Ingénieur de Recherche CNRS HNHP UMR 7194 (MnHn)
</td>
<td><a href="yannick.miras@mnhn.fr">yannick.miras@mnhn.fr</a></td>
<tr>
<tr>
<td>
Engelbert Mephu Nguifo<br />
Professeur des Universités
Professeur UCA LIMOS UMR 6158 (UCA)
</td>
<td>LIMOS - Clermont-Ferrand</td>
<td><a href="mailto:engelbert.mephu_nguifo@uca.fr">engelbert.mephu_nguifo@uca.fr</a></td>
<tr>
</tr>
<td>
Jean-Luc Peiry<br />
Professeur des Universités
</td>
<td>
GEOLAB - Clermont-Ferrand<br />
désormais : ESS-Dakar
Professeur UCA EDYTEM UMR 5204
<td>
<a href="mailto:jean-luc.peiry@cnrs.fr">jean-luc.peiry@cnrs.fr</a>
<a href="mailto:J-Luc.PEIRY@uca.fr">J-Luc.PEIRY@uca.fr</a>
</td>
</tr>
<tr>
<td>
Marie Pailloux<br />
Maître de conférences
</td>
<td>
LIMOS - Clermont-Ferrand
Maître de conférences UCA LIMOS UMR 6158 (UCA)
</td>
<td>
<a href="mailto:pailloux@isima.fr">pailloux@isima.fr</a>
......@@ -64,10 +48,7 @@
<tr>
<td>
Delphine Latour<br />
Maître de conférences
</td>
<td>
LMGE - Clermont-Ferrand
Maître de conférences UCA LMGE UMR 6023 (UCA)
</td>
<td>
<a href="mailto:Delphine.LATOUR@uca.fr">Delphine.LATOUR@uca.fr</a>
......@@ -75,11 +56,8 @@
</tr>
<tr>
<td>
Aude Beauger
Ingénieur de Recherche
</td>
<td>
GEOLAB - Clermont-Ferrand
Aude Beauger<br />
Ingénieur de Recherche CNRS GEOLAB UMR 6042 (UCA)
</td>
<td>
<a href="mailto:aude.beauger@uca.fr">aude.beauger@uca.fr</a>
......@@ -88,10 +66,7 @@
<tr>
<td>
Vincent Mazenod<br />
Ingénieur de Recherche
</td>
<td>
LIMOS - Clermont-Ferrand
Ingénieur de Recherche UCA LIMOS UMR 6158 (UCA)
</td>
<td>
<a href="mailto:vincent.mazenod@uca.fr">vincent.mazenod@uca.fr</a>
......
......@@ -5,44 +5,6 @@
<h2><i class="fas fa-book-open"></i> Project's publications</h2>
<ul id="publications">
<li>
Lonlac Konlac J., Miras Y., Beauger A., Peiry J.L., Mephu-Nguifo E., 2017.
Une approche d’extraction des motifs graduels fermés fréquents sous contrainte
de la temporalité. Revue des Nouvelles Technologies de l’Information,
vol RNTI-E-33, EGC 2017 : 213-224.
</li>
<li>
Lonlac J., Miras Y., Beauger A., Mazenod V., Peiry J.L., Mephu-Nguifo E., 2018.
An Approach for Extracting Frequent (Closed) Gradual Patterns Under Temporal Constraint.
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE),
Rio de Janeiro, Brazil, July 8-13, 1-8.
</li>
<li>
Miras Y., Beauger A., Legrand B., Latour D., Serieyssol K., Lavrieux M., Ledger P.,
Peiry J.L., Mephu-Nguifo E., Lonlac Konlac J., in revisión.
Patterning Holocene lake dynamics and detecting early Prehistoric human impacts:
targets of an improved integration of multivariate ecological indicators thanks to the data mining approach.
Quaternary International.
</li>
</ul>
<hr />
<h3><i class="fas fa-asterisk"></i> References</h3>
<ul id="references">
<li>
Agrawal R., Srikant R., 1994. Fast algorithms for mining association rules in large databases.
Proceedings of the 20th VLDB Conference, Santiago de Chile, Chile, September 12-15, 487-499.
</li>
<li>
Berzal F., Cubero J.C., Sánchez D., Miranda M.A.V., Serrano J., 2007.
An alternative approach to discover gradual dependencies. International Journal of Uncertainty,
Fuzziness and Knowledge-Based Systems 15, 5: 559-570.
</li>
<li>
Di-Jorio, L., Laurent, A., Teisseire, M., 2008.
Fast extraction of gradual association rules: a heuristic based method. CSTST, Cergy-Pontoise, France, October 28-31, 205–210.
</li>
<li>
Lonlac Konlac J., Miras Y., Beauger A., Peiry J.L., Mephu-Nguifo E., 2017.
Une approche d’extraction des motifs graduels fermés fréquents sous contrainte de la temporalité.
......@@ -51,15 +13,16 @@
<li>
Lonlac J., Miras Y., Beauger A., Mazenod V., Peiry J.L., Mephu-Nguifo E., 2018.
An Approach for Extracting Frequent (Closed) Gradual Patterns Under Temporal Constraint.
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Rio de Janeiro, Brazil, July 8-13, 1-8.
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Rio de Janeiro, Brazil,
July 8-13, 1-8.
</li>
<li>
Pasquier, N., Bastide, R., Taouil, R., Lakhal, L., 1999.
Discovering frequent closed itemsets for association rules.
ICDT, Jerusalem, Israel, January 10-12, 398-416.
Miras Y., Beauger A., Legrand B., Latour D., Serieyssol K., Lavrieux M., Ledger P.,
Peiry J.L., Mephu-Nguifo E., Lonlac Konlac J., 2022.
Tracking plant, fungal and algal diversity through a data mining approach:
towards improved analysis of holocene lake Aydat (Puy-de-Dôme, France) dynamics
and ecological legacies. Revue des Sciences naturelles d'Auvergne.
</li>
</ul>
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