Commit b88349b6 authored by Vincent Mazenod's avatar Vincent Mazenod
Browse files

split menu in several tabs

parent 4ab18ab7
......@@ -17,4 +17,9 @@ span.index {
.icon-circle.bg-primary {
padding-left: 5px;
padding-right: 5px;
}
#members td {
padding: 10px;
border-bottom: 1px solid white;
}
\ No newline at end of file
......@@ -25,4 +25,37 @@ class DocsController extends AbstractController
{
return $this->render('docs/tutorial.html.twig');
}
/**
* @Route("/members", name="members")
*/
public function members()
{
return $this->render('docs/members.html.twig');
}
/**
* @Route("/publications", name="publications")
*/
public function publications()
{
return $this->render('docs/publications.html.twig');
}
/**
* @Route("/description", name="description")
*/
public function description()
{
return $this->render('docs/description.html.twig');
}
/**
* @Route("/references", name="references")
*/
public function references()
{
return $this->render('docs/references.html.twig');
}
}
......@@ -32,101 +32,6 @@
To use MOBI-PALEO, you must first <a href="{{ path('register') }}">register</a> before <a href="{{ path('login') }}">logging</a> in.
</p>
<h2>MEMBERS</h2>
<h2>PUBLICATIONS</h2>
<ul>
<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>
<h2>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.
</p>
<h3>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>
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>
Pasquier, N., Bastide, R., Taouil, R., Lakhal, L., 1999.
Discovering frequent closed itemsets for association rules.
ICDT, Jerusalem, Israel, January 10-12, 398-416.
</li>
</ul>
<h2>CONTACT</h2>
......
{% extends 'base.html.twig' %}
{% block body %}
<h2>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.
</p>
{% endblock %}
\ No newline at end of file
{% extends 'base.html.twig' %}
{% block body %}
<h2>MEMBERS</h2>
<table id="members">
<tr>
<td>
Jerry Lonlac Konlac<br />
Post-doctoral
</td>
<td>
GEOLAB - LIMOS - Clermont-Ferrand<br />
now : IMT Lille Douai
</td>
<td>
<a href="mailto:jerry.lonlac@imt-lille-douai.fr">jerry.lonlac@imt-lille-douai.fr</a>
</td>
</tr>
<tr>
<td>
Yannick Miras<br />
Research engineer
</td>
<td>
GEOLAB - Clermont-Ferrand<br />
désormais : HNPN-Paris
</td>
<td>yannick.miras@mnhn.fr</td>
<tr>
<tr>
<td>
Engelbert Mephu Nguifo<br />
University professor
</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 />
University professor
</td>
<td>
GEOLAB - Clermont-Ferrand<br />
désormais : ESS-Dakar
<td>
<a href="mailto:jean-luc.peiry@cnrs.fr">jean-luc.peiry@cnrs.fr</a>
</td>
</tr>
<tr>
<td>
Marie Pailloux<br />
Lecturer
</td>
<td>
LIMOS - Clermont-Ferrand
</td>
<td>
<a href="mailto:pailloux@isima.fr">pailloux@isima.fr</a>
</td>
</tr>
<tr>
<td>
Delphine Latour<br />
Lecturer
</td>
<td>
LMGE - Clermont-Ferrand
</td>
<td>
<a href="mailto:Delphine.LATOUR@uca.fr">Delphine.LATOUR@uca.fr</a>
</td>
</tr>
<tr>
<td>
Aude Beauger
Research engineer
</td>
<td>
GEOLAB - Clermont-Ferrand
</td>
<td>
<a href="mailto:aude.beauger@uca.fr">aude.beauger@uca.fr</a>
</td>
</tr>
<tr>
<td>
Vincent Mazenod<br />
Research engineer
</td>
<td>
LIMOS - Clermont-Ferrand
</td>
<td>
<a href="mailto:vincent.mazenod@uca.fr">vincent.mazenod@uca.fr</a>
</td>
</tr>
</table>
{% endblock %}
\ No newline at end of file
{% extends 'base.html.twig' %}
{% block body %}
<h2>PUBLICATIONS</h2>
<ul>
<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>
{% endblock %}
\ No newline at end of file
{% extends 'base.html.twig' %}
{% block body %}
<h3>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>
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>
Pasquier, N., Bastide, R., Taouil, R., Lakhal, L., 1999.
Discovering frequent closed itemsets for association rules.
ICDT, Jerusalem, Israel, January 10-12, 398-416.
</li>
</ul>
{% endblock %}
\ No newline at end of file
......@@ -43,6 +43,50 @@
</a>
</li>
<li class="nav-item">
<a class="nav-link
{% if app.request.attributes.get('_route') == 'members' %}
active
{% endif %}"
href="{{ path('members') }}">
<i class="fas fa-book"></i>
members
</a>
</li>
<li class="nav-item">
<a class="nav-link
{% if app.request.attributes.get('_route') == 'publications' %}
active
{% endif %}"
href="{{ path('publications') }}">
<i class="fas fa-book"></i>
publications
</a>
</li>
<li class="nav-item">
<a class="nav-link
{% if app.request.attributes.get('_route') == 'description' %}
active
{% endif %}"
href="{{ path('description') }}">
<i class="fas fa-book"></i>
description
</a>
</li>
<li class="nav-item">
<a class="nav-link
{% if app.request.attributes.get('_route') == 'references' %}
active
{% endif %}"
href="{{ path('references') }}">
<i class="fas fa-book"></i>
references
</a>
</li>
{% if is_granted('IS_AUTHENTICATED_FULLY') %}
<li class="nav-item">
<a class="nav-link
......@@ -101,12 +145,6 @@
{% else %}
<li class="nav-item">
<a class="nav-link" href="{{ path('login') }}">
<i class="fas fa-sign-in-alt"></i>
login
</a>
</li>
<li class="nav-item">
<a class="nav-link" href="{{ path('register') }}">
<i class="fas fa-user-plus"></i>
......@@ -114,6 +152,14 @@
</a>
</li>
<li class="nav-item">
<a class="nav-link" href="{{ path('login') }}">
<i class="fas fa-sign-in-alt"></i>
login
</a>
</li>
{% endif %}
</ul>
......
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