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- Jilles Vreeken, Matthijs van Leeuwen, Arno Siebes
- Data Mining and Knowledge Discovery
- 2010

One of the major problems in pattern mining is the explosion of the number of results. Tight constraints reveal only common knowledge, while loose constraints lead to an explosion in the number of returned patterns. This is caused by large groups of patterns essentially describing the same set of transactions. In this paper we approach this problem using… (More)

- M. Wiering, J. Vreeken, J. van Veenen, A. Koopman
- IEEE Intelligent Vehicles Symposium, 2004
- 2004

Optimal traffic light control is a multi-agent decision problem, for which we propose to use reinforcement learning algorithms. Our algorithm learns the expected waiting times of cars for red and green lights at each intersection, and sets the traffic lights to green for the configuration maximizing individual car gains. For testing our adaptive traffic… (More)

Spotting anomalies in large multi-dimensional databases is a crucial task with many applications in finance, health care, security, etc. We introduce COMPREX, a new approach for identifying anomalies using pattern-based compression. Informally, our method finds a collection of dictionaries that describe the norm of a database succinctly, and subsequently… (More)

One of the major problems in frequent item set mining is the explosion of the number of results: it is difficult to find the most interesting frequent item sets. The cause of this explosion is that large sets of frequent item sets describe essentially the same set of transactions. In this paper we approach this problem using the MDL principle: the best set… (More)

- Michael Mampaey, Nikolaj Tatti, Jilles Vreeken
- KDD
- 2011

Data analysis is an inherently iterative process. That is, what we know about the data greatly determines our expectations, and hence, what result we would find the most interesting. With this in mind, we introduce a well-founded approach for succinctly summarizing data with a collection of itemsets; using a probabilistic maximum entropy model, we… (More)

Vehicular travel is increasing throughout the world, particularly in large urban areas. Therefore the need arises for simulating and optimizing traffic control algorithms to better accommodate this increasing demand. In this paper we study the simulation and optimization of traffic light controllers in a city and present an adaptive optimization algorithm… (More)

- B. Aditya Prakash, Jilles Vreeken, Christos Faloutsos
- 2012 IEEE 12th International Conference on Data…
- 2012

Given a snapshot of a large graph, in which an infection has been spreading for some time, can we identify those nodes from which the infection started to spread? In other words, can we reliably tell who the culprits are? In this paper we answer this question affirmatively, and give an efficient method called NETSLEUTH for the well-known… (More)

- Pauli Miettinen, Jilles Vreeken
- KDD
- 2011

Matrix factorizations---where a given data matrix is approximated by a product of two or more factor matrices---are powerful data mining tools. Among other tasks, matrix factorizations are often used to separate global structure from noise. This, however, requires solving the `model order selection problem' of determining where fine-grained structure stops,… (More)

- Danai Koutra, U. Kang, Jilles Vreeken, Christos Faloutsos
- SDM
- 2014

How can we succinctly describe a million-node graph with a few simple sentences? How can we measure the ‘importance’ of a set of discovered subgraphs in a large graph? These are exactly the problems we focus on. Our main ideas are to construct a ‘vocabulary’ of subgraph-types that often occur in real graphs (e.g., stars, cliques, chains), and from a set of… (More)

- Koen Smets, Jilles Vreeken
- SDM
- 2011

In many situations there exists an abundance of positive examples, but only a handful of negatives. In this paper we show how in binary or transaction data such rare cases can be identified and characterised. Our approach uses the Minimum Description Length principle to decide whether an instance is drawn from the training distribution or not. By using… (More)