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Willy Ugarte Rojas
Actually I'm working at Peruvian University of Applied Sciences as full-time Professor.
Before I worked at the University of Grenoble Alpes as post-doctoral lecturer with Professor Alexandre Termier (University of Rennes I), Professor Marie-Christine Rousset (University of Grenoble Alpes) and PhD Miguel Santana (SMTMicroelectronics).
My work is about the application of data mining techniques to trace analysis in motherboard chips.
Before I have worked in the University of Caen in using soft constraints and optimisation techniques for pattern mining, through the application of Constraint Programming to Data Mining, my PhD thesis was under supervision of Professor Patrice Boizumault, Professor Bruno Crémilleux and Associate Professor Samir Loudni.
Supervisors: Patrice Boizumault, Bruno Cremilleux, Samir Loudni, Alexandre Termier, and Miguel Santana
Address: http://ugarte.users.greyc.fr/
Before I worked at the University of Grenoble Alpes as post-doctoral lecturer with Professor Alexandre Termier (University of Rennes I), Professor Marie-Christine Rousset (University of Grenoble Alpes) and PhD Miguel Santana (SMTMicroelectronics).
My work is about the application of data mining techniques to trace analysis in motherboard chips.
Before I have worked in the University of Caen in using soft constraints and optimisation techniques for pattern mining, through the application of Constraint Programming to Data Mining, my PhD thesis was under supervision of Professor Patrice Boizumault, Professor Bruno Crémilleux and Associate Professor Samir Loudni.
Supervisors: Patrice Boizumault, Bruno Cremilleux, Samir Loudni, Alexandre Termier, and Miguel Santana
Address: http://ugarte.users.greyc.fr/
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The discovery of local patterns has been modeled as a constraint satisfaction problem (CSP), and then extended to the extraction of n-ary patterns . Then, a query language based on high-level constraints has been proposed.
Firstly, the soft thresholds were introduced in pattern mining through the disjunctive relaxation. Then, the skypattern mining problem is treated using dynamic CSP. In addition, the extraction of the skypattern cube (union of skypatterns sets of all possible combinations for a set of measures) is proposed, (i) with a level-wise approach by taking advantage of derivation rules and (ii) using the relationship between the skypattern cube and edge-skypatterns. The use of dynamic CSP to extract Pareto-optimal patterns (skypatterns) has been extended to extract the optimal patterns with respect to a preference.
The main contributions of this framework are its declarativity and genericity . It is the first generic and flexible framework for the use of softness in data mining.
Université de Caen/Basse-Normandie
University of Saint Antonio Abad of Cusco (UNSAAC)
One of its most important subfields, pattern mining, involves searching and enumerating interesting patterns in data.
Various aspects of pattern mining are studied in the theory of computation and statistics.
In the last decade, the pattern mining community has witnessed a sharp shift from efficiency-based approaches to methods which can extract more meaningful patterns.
Recently, new methods adapting results from studies of economic efficiency and multi-criteria decision analyses such as Pareto efficiency, or skylines, have been studied.
Within pattern mining, this novel line of research allows the easy expression of preferences according to a dominance relation.
This approach is useful from a user-preference point of view and tends to promote the use of pattern mining algorithms for non-experts.
We present a significant extension of our previous work [1,2] on the discovery of skyline patterns (or "skypatterns'') based on the theoretical relationships with condensed representations of patterns.
We show how these relationships facilitate the computation of skypatterns and we exploit them to propose a flexible and efficient approach to mine skypatterns using a dynamic constraint satisfaction problems (CSP) framework.
We present a unified methodology of our different approaches towards the same goal.
This work is supported by an extensive experimental study allowing us to illustrate the strengths and weaknesses of each approach.
(OPs), defined as the best patterns according to a given user
preference, and show that OPs encompass many data mining
problems. Then, we propose a generic method based on a
Dynamic Constraint Satisfaction Problem to mine OPs, and
we show that any OP is characterized by a basic constraint
and a set of constraints to be dynamically added. Finally, we
perform an experimental study comparing our approach vs ad
hoc methods on several types of OPs.
Our approach is based on the fact that each node of the cube can be approximated by the set of edge-skypatterns (a relaxed form of skypatterns) w.r.t. the whole set of measures M.
Then we transform the problem into a skyline cube mining in |M| dimensions.
The set of edge-skypatterns can be efficiently mined by using either a dynamic CSP method or an extended version of a static method based on the theoretical relationships between patterns and condensed representations of skypatterns.
Experiments conducted on UCI datasets and on a real-life dataset (Mutagenicity) show the relevance and performance of our approach.
that would be missed otherwise. Then, thanks to Constraint Programming, we propose a generic and efficient method to mine skypatterns as well as soft ones. Finally, we show the relevance and the effectiveness of our approach through experiments on UCI benchmarks and a case study in chemoinformatics for discovering toxicophores.
efficient bottom-up approach to compute them. Our ap-
proach relies on derivation rules collecting skypatterns
of a parent node from its child nodes without any do-
minance test. Non-derivable skypatterns are computed
on the fly thanks to Dynamic CSP. The bottom-up prin-
ciple enables to provide a concise representation of the
cube based on skypattern equivalence classes without
any supplementary effort. Experiments on mutagenicity
datasets show the effectiveness of our proposal.
pour la découverte de toxicophores."
The discovery of local patterns has been modeled as a constraint satisfaction problem (CSP), and then extended to the extraction of n-ary patterns . Then, a query language based on high-level constraints has been proposed.
Firstly, the soft thresholds were introduced in pattern mining through the disjunctive relaxation. Then, the skypattern mining problem is treated using dynamic CSP. In addition, the extraction of the skypattern cube (union of skypatterns sets of all possible combinations for a set of measures) is proposed, (i) with a level-wise approach by taking advantage of derivation rules and (ii) using the relationship between the skypattern cube and edge-skypatterns. The use of dynamic CSP to extract Pareto-optimal patterns (skypatterns) has been extended to extract the optimal patterns with respect to a preference.
The main contributions of this framework are its declarativity and genericity . It is the first generic and flexible framework for the use of softness in data mining.
Université de Caen/Basse-Normandie
University of Saint Antonio Abad of Cusco (UNSAAC)
One of its most important subfields, pattern mining, involves searching and enumerating interesting patterns in data.
Various aspects of pattern mining are studied in the theory of computation and statistics.
In the last decade, the pattern mining community has witnessed a sharp shift from efficiency-based approaches to methods which can extract more meaningful patterns.
Recently, new methods adapting results from studies of economic efficiency and multi-criteria decision analyses such as Pareto efficiency, or skylines, have been studied.
Within pattern mining, this novel line of research allows the easy expression of preferences according to a dominance relation.
This approach is useful from a user-preference point of view and tends to promote the use of pattern mining algorithms for non-experts.
We present a significant extension of our previous work [1,2] on the discovery of skyline patterns (or "skypatterns'') based on the theoretical relationships with condensed representations of patterns.
We show how these relationships facilitate the computation of skypatterns and we exploit them to propose a flexible and efficient approach to mine skypatterns using a dynamic constraint satisfaction problems (CSP) framework.
We present a unified methodology of our different approaches towards the same goal.
This work is supported by an extensive experimental study allowing us to illustrate the strengths and weaknesses of each approach.
(OPs), defined as the best patterns according to a given user
preference, and show that OPs encompass many data mining
problems. Then, we propose a generic method based on a
Dynamic Constraint Satisfaction Problem to mine OPs, and
we show that any OP is characterized by a basic constraint
and a set of constraints to be dynamically added. Finally, we
perform an experimental study comparing our approach vs ad
hoc methods on several types of OPs.
Our approach is based on the fact that each node of the cube can be approximated by the set of edge-skypatterns (a relaxed form of skypatterns) w.r.t. the whole set of measures M.
Then we transform the problem into a skyline cube mining in |M| dimensions.
The set of edge-skypatterns can be efficiently mined by using either a dynamic CSP method or an extended version of a static method based on the theoretical relationships between patterns and condensed representations of skypatterns.
Experiments conducted on UCI datasets and on a real-life dataset (Mutagenicity) show the relevance and performance of our approach.
that would be missed otherwise. Then, thanks to Constraint Programming, we propose a generic and efficient method to mine skypatterns as well as soft ones. Finally, we show the relevance and the effectiveness of our approach through experiments on UCI benchmarks and a case study in chemoinformatics for discovering toxicophores.
efficient bottom-up approach to compute them. Our ap-
proach relies on derivation rules collecting skypatterns
of a parent node from its child nodes without any do-
minance test. Non-derivable skypatterns are computed
on the fly thanks to Dynamic CSP. The bottom-up prin-
ciple enables to provide a concise representation of the
cube based on skypattern equivalence classes without
any supplementary effort. Experiments on mutagenicity
datasets show the effectiveness of our proposal.
pour la découverte de toxicophores."