ORBEL, the Belgian O.R. Society (Sogesci-B.V.W.B.) organised a

Schedule

08.30 - 09.00	Registration
09.00 - 09.10	Welcome address
09.10 - 09.55	Toon Calders (UA), Frequent pattern mining, abstract, slides
10.00 - 10.45	Gianluca Bontempi (ULB), Feature selection methods for mining bio-informatics data, abstract, slides
10.50 - 11.35	Koen Vanhoof (LUC), A framework to improve product assortment decisions, abstract, slides
11.40 - 12.25	Emilio Carrizosa (Universidad de Sevilla, Spain), Arbitrary-norm support vector machine. Properties and Applications, abstract, slides
12.30 - 13.45	Lunch
14.00 - 14.45	Luc De Raedt (Universität Freiburg, Germany), Constraint-based data mining and inductive databases with an application in molecular feature mining, abstract, slides
14.50 - 15.35	Louis Wehenkel (ULg), Decision and regression tree ensemble methods and their application in automatic learning, abstract, slides
15.40 - 16.25	Marco Saerens (UCL), The principal components analysis of a graph: Application to a collaborative recommendation task, abstract, slides

Location: The symposium will be held at the Facultés Universitaires Saint-Louis (FUSL), 43 Boulevard du Jardin Botanique, B-1000 Brussels, located close to the Brussels North railway station ; see the following access map. The symposium will take place in the Salle des Examens.

Registration: Registration in advance is required. Please fill in the following registration form and sent it as an attachment of an email to the symposium organiser Gerrit Janssens at [email protected].

Subscription fees: 30 Euro for members of the Sogesci-BVWB or for students; 50 Euro for non-members non-students. The fees have to be paid on the account from the KBC with number 403-4055261-55 with the message DMOR + participant name, or on location. An invoice will be sent on request by e-mail to Gerrit Janssens at [email protected]. The subscription fee includes participation to the conference, documentation material on the talks on paper, coffee or soft drinks and a lunch.

Further questions should be addressed to the symposium organiser Gerrit Janssens at [email protected].

Abstracts

• Toon Calders (UA), Frequent pattern mining
  (download the slides of the presentation)

  In recent years, mining frequent itemsets has been one of the most active research areas in data mining. In my talk I will give a (biased) overview of the research in this area. The survey will include: depth-first versus breadth-first algorithms, mining with constraints, mining maximal frequent itemsets, closed sets, and mining more complex data types and patterns. Throughout the talk, the focus will be on motivating the different research directions and providing intuition for the proposed solutions and algorithms, rather than on technical details.

• Gianluca Bontempi (ULB), Feature selection methods for mining bio-informatics data
  (download the slides of the presentation)

  The use of data mining techniques in bioinformatics is continuously confronted with the problem of managing datasets where the number of features is much larger than the number of samples (high feature-to-sample ratio datasets). The talk will first discuss some examples (from inference of regulatory networks to discrimination in cancer classification) and then will focus on some issues to be be taken into account for effectively dealing with this type of data.

• Koen Vanhoof (LUC), A framework to improve product assortment decisions
  (download the slides of the presentation)

  It has been claimed that the discovery of association rules is well suited for applications of market basket analysis to reveal regularities in the purchase behaviour of customers. However today, one disadvantage of associations discovery is that there is no provision for taking into account the business value of an association. Therefore, recent work indicates that the discovery of interesting rules can in fact best be addressed within a microeconomic framework. This study integrates the discovery of frequent itemsets with a (microeconomic) model for product selection (PROFSET). The model enables the integration of both quantitative and qualitative (domain knowledge) criteria. Sales transaction data from a fully automated convenience store are used to demonstrate the effectiveness of the model against a heuristic for product selection based on product-specific profitability. We show that with the use of frequent itemsets we are able to identify the cross-sales potential of product items and use this information for better product selection. Furthermore, we demonstrate that the impact of product assortment decisions on overall assortment profitability can easily be evaluated by means of sensitivity analysis.

• Emilio Carrizosa (Universidad de Sevilla, Spain), Arbitrary-norm support vector machine. Properties and Applications
  (download the slides of the presentation)

  In this talk we will explore the optimization problems found in Support Vector Machines (SVM) when distances are measured via arbitrary (not necessarily Euclidean) norms. Particular attention will be given to the polyhedral case, since the optimization problems obtained are transformed into Linear Problems, thus solvable via standard LP optimizers. The application of the SVM paradigm to the construction of an interactive multicriteria algorithm will be also discussed.

• Luc De Raedt (Universität Freiburg, Germany) Constraint-based data mining and inductive databases with an application in molecular feature mining
  (download the slides of the presentation)

  Constraint based mining and inductive databases are a recent research stream within data mining. It aims at supporting the knowledge discovery process by means of declarative inductive queries, which are used for querying for patterns. Various types of constraints (and corresponding solvers) will be introduced. A case study in the field of molecular feature mining will be presented in the system MolFea.
  This talk will be based on De Raedt, L. A perspective on inductive databases, SIGKDD Explorations, 4(2), 2002 and related papers.

• Louis Wehenkel (ULg), Decision and regression tree ensemble methods and their application in automatic learning
  (download the slides of the presentation)

  The talk presents a new supervised learning algorithm called Extra-Trees (extremely randomized trees), which builds ensembles of decision or regression trees by randomizing the choice of attribute and cut-point. After the discussion of the main properties of this method, we present three different applications: tree-based batch mode reinforcement learning; pixel-based image classification, and biomarker identification in proteomics.

• Marco Saerens (UCL), The principal components analysis of a graph: Application to a collaborative recommendation task
  (download the slides of the presentation)

  This work presents some general procedures for computing dissimilarities/similarities between elements of a database or, more generally, nodes of a weighted, undirected, graph. It is based on a Markov-chain model of random walk through the database. The model assigns transition probabilities to the links between elements, so that a random walker can jump from element to element. Unlike the standard "shortest path" distance, these quantities, representing similarities between any two elements, have the nice property of decreasing (increasing) when the number of paths connecting these two elements increases and when the "length" of any path decreases. We also define the principal component analysis (PCA) of a graph as the subspace projection that preserves as much variance as possible, in terms of the defined quantity. This PCA has some interesting links with spectral graph theory, in particular "spectral clustering". The model is applied on a collaborative recommendation task where suggestions are made about which movies people should watch based upon what they watched in the past. Experimental results on the MovieLens database show that the Laplacian pseudoinverse-based similarity outperforms the other methods.

List of participants: contact information (address, email) for each participant is available in the following PDF list.

• BONTEMPI Gianluca, Département d'Informatique, Machine Learning Group, Université Libre de Bruxelles
• CALDERS Toon, Dept. Wiskunde-Informatica, Universiteit Antwerpen
• CARRIZOSA Emilio, Faculdad de Matematicas, Universidad de Sevilla
• CRANINX Michel, Universiteit Gent
• CURVERS Daan, Universiteit Gent
• DE BAETS Bernard, Department of Applied Mathematics, Biometrics and Process Control, Universiteit Gent
• DE BRUYNE Steven, Vakgroep Wiskunde, Operationeel Onderzoek, Statistiek en Informatica, Vrije Universiteit Brussel
• DE RAEDT Luc, Institut für Informatik, Universität Freiburg
• DE VOS Daniella, Industrial Management, Universiteit Gent
• DE WEIRDT Marjolein, Universiteit Gent
• GEURTS Pierre, Institut Montefiore, Université de Liège
• GLINEUR François, Center for Operations Research and Econometrics, Université Catholique de Louvain
• JANSSENS Gerrit, Departement Bedrijfskunde, Limburgs Universitair Centrum
• JANSSENS Saskia, Department of Applied Mathematics, Biometrics and Process Control, Universiteit Gent
• LAVENDHOMME Thierry, Facultés Universitaires Saint-Louis
• LOOMAN Brecht, Universiteit Gent
• LOUTE Etienne, Facultés Universitaires Saint-Louis
• MAREE Raphael, Institut Montefiore, Université de Liège
• NOWE Ann, Vakgroep Informatica en Toegepaste Informatica, Vrije Universiteit Brussel
• PASTOR Franck, Facultés Universitaires Saint-Louis
• PLASTRIA Frank, Vakgroep Wiskunde, Operationeel Onderzoek, Statistiek en Informatica, Vrije Universiteit Brussel
• ROOSE Frederik, Universiteit Gent
• SAERENS Marco, School of Management (IAG), Université Catholique de Louvain
• SAVEYN Pieter, Universiteit Gent
• VANHAVERBEKE Lieselot, Vakgroep Wiskunde, Operationeel Onderzoek, Statistiek en Informatica, Vrije Universiteit Brussel
• VANHOOF Koen, Departement Bedrijfskunde, Limburg Universitair Centrum
• VERTOMMEN Joris, Centrum voor Industrieel Beleid, Katholieke Universiteit Leuven
• WEHENKEL Louis, Institut Montefiore, Université de Liège

Access map

The symposium will take place in building number 1 on the map, in the exam room (Salles des examens), on the second floor. There will signs leading to it: after entrance, go through the next door in front of you. Then take the lift on your right, just after the main secretariat door. Exit the lift at the second floor (according to European floor metric!) on your left. The room is the exam room with a double door, just in front of you.