Abstract Tetris and Decidability ⋆ (2008)
Hendrik Jan Hoogeboom, Walter A. Kosters
We consider a variant of Tetris where the sequence of pieces (together with their orientation and horizontal position, which cannot be changed anymore) is generated by a finite state automaton. We...
to mine frequent patterns in unaligned protein sequences (2008)
An Efficient, Kai Ye, Walter A. Kosters, Adriaan P. Ijzerman
Motivation: Pattern discovery in protein sequences is often based on multiple sequence alignments (MSA). The procedure can be computationally intensive and often requires manual adjustment, which may...
Displaying Co-occurrences of Patterns in Streams for Website Usage Analysis (2008)
Edgar H. Graaf, Joost N. Kok, Walter A. Kosters
One way of getting a better view of data is by using frequent patterns. In this paper frequent patterns are (sub)sets that occur a minimal number of times in a stream of itemsets. However, the...
Frequent Itemsets for Genomic Profiling (2008)
Judith M. Boer, Walter A. Kosters
Abstract. Frequent itemset mining is a promising approach to the study of genomic profiling data. Here a dataset consists of real numbers describing the relative level in which a clone occurs in...
Detecting and Pruning Introns for Faster Decision Tree Evolution (2008)
Jeroen Eggermont, Joost N. Kok, Walter A. Kosters
Abstract. We show how the understandability and speed of genetic programming classification algorithms can be improved, without affecting the classification accuracy. By analyzing the decision trees...
Theory of Genetic Algorithms ---extended --- (2007)
Thomas Back, Joost N. Kok, Walter A. Kosters
this paper we introduce some methods for examining the fundamental properties of genetic algorithms ([Hol75, Jon75, Gol89, Mit96]), probabilistic search and optimization algorithms that work on...
Nieuwsbrief van de Nederlandse Vereniging voor Theoretische Informatica (2007)
Mieke Brune, Jan Willem Klop, Jan Rutten (eds.), Van De Redactie, Samenstelling Bestuur, ...
s van voordrachten On Notions of bisimulation and associated logics Prof.dr. M. Nielsen BRICS, Computer Science Department, University of Aarhus Recently, there have been several attempts of abstract...
Genetic Programming Produces Strategies for Agents in a Dynamic Environment (2007)
Robert E. Keller, Walter A. Kosters, Martijn Vaart
We introduce an agent-based approach to game playing where every agent is an element of a game and is driven by a control algorithm that is being adapted by Genetic Programming, without using an...
Nonmetric multidimensional scaling with neural networks (2007)
Abstract In this paper we present a neural network for nonmetric multidimensional scaling. In our approach, the monotone transformation that is a part of every nonmetric scaling algorithm is...
A Theoretical and Practical Comparison (2007)
Of Depth First, Of Apriori, Walter A. Kosters, Wim Pijls, Viara Popova
We examine the complexity of Depth First and FP-growth implementations of Apriori, two of the fastest known data mining algorithms to nd frequent itemsets in large databases. We describe the...
Tetris is Hard, Even to Approximate (2007)
Ron Breukelaar, Erik D. Demaine, Susan Hohenberger, Hendrik Jan Hoogeboom, Walter A. Kosters, David Liben-nowell
In the popular computer game of Tetris, the player is given a sequence of tetromino pieces and must pack them into a rectangular gameboard initially occupied by a given configuration of filled...
Clustering Co-occurrence of Maximal Frequent Patterns in Streams (2007)
De Graaf, Edgar H., Kok, Joost N., Kosters, Walter A.
One way of getting a better view of data is using frequent patterns. In this paper frequent patterns are subsets that occur a minimal number of times in a stream of itemsets. However, the discovery...
Clustering with Lattices in the Analysis of Graph Patterns (2007)
De Graaf, Edgar H., Kok, Joost N., Kosters, Walter A.
Mining frequent subgraphs is an area of research where we have a given set of graphs (each graph can be seen as a transaction), and we search for (connected) subgraphs contained in many of these...
Ye, Kai, Kosters, Walter A., IJzerman, Adriaan P.
Motivation: Pattern discovery in protein sequences is often based on multiple sequence alignments (MSA). The procedure can be computationally intensive and often requires manual adjustment, which may...
Joost Broekens, Walter A. Kosters, Fons J. Verbeek
Abstract. Psychological studies have shown that emotion and affect influence learning. We employ these findings in a machine-learning metaparameter context, and dynamically couple an adaptive...
Joost Broekens, Walter A. Kosters, Fons J. Verbeek, Joost Broekens, Walter A. Kosters, Fons J. Verbeek
On behalf of:
Ye, Kai, Kosters, Walter A., IJzerman, Adriaan P.
Motivation: Pattern discovery in protein sequences is often based on multiple sequence alignments (MSA). The procedure can be computationally intensive and often requires manual adjustment, which may...
Consecutive Support: Better Be Close! (2006)
De Graaf, Edgar, De Graaf, Jeannette, Kosters, Walter A.
We propose a new measure of support (the number of occur- rences of a pattern), in which instances are more important if they occur with a certain frequency and close after each other in the stream...
Data mining approaches to criminal career analysis (2006)
Tim K. Cocx, Walter A. Kosters, Joost N. Kok
Abstract — Narrative reports and criminal records are stored digitally across individual police departments, enabling the collection of this data to compile a nation-wide database of criminals and...
Data mining approaches to criminal career analysis (2006)
Tim K. Cocx, Walter A. Kosters, Joost N. Kok
Abstract — Narrative reports and criminal records are stored digitally across individual police departments, enabling the collection of this data to compile a nation-wide database of criminals and...
Object-centered interactive multi-dimensional scaling: Ask the expert (2006)
Joost Broekens, Tim Cocx, Walter A. Kosters
Multi-dimensional scaling (MDS) is a widely used technique to show, in a low dimensional space, relations between objects—such as humans, documents, soil samples—that are defined by a large set...
How to find frequent patterns? (2005)
An improved version of DF, the depth first implementation of Apriori as devised in [7], is presented. Given a database of (e.g., supermarket) transactions, the DF algorithm builds a so-called trie...
Hendrik Jan Hoogeboom, Walter A. Kosters
{ hoogeboo,kosters} @ liacs. nl
Genetic Programming for Data Classification: Partitioning the Search Space (2004)
Jeroen Eggermont, Joost N. Kok, Walter A. Kosters
When Genetic Programming is used to evolve decision trees for data classification, search spaces tend to become extremely large. We present several methods using techniques from the field of machine...
Tetris is hard, made easy (2003)
Ron Breukelaar, Hendrik Jan Hoogeboom, Walter A. Kosters
Abstract. In their paper “Tetris is Hard, Even to Approximate ” [2] Demaine, Hohenberger and Liben-Nowell show that optimally playing the “offline ” version of Tetris, where the initial board...
c ○ World Scientific Publishing Company TETRIS IS HARD, EVEN TO APPROXIMATE ∗ (2003)
Ron Breukelaar, Erik D. Demaine, Susan Hohenberger, Hendrik Jan Hoogeboom, Walter A. Kosters, David Liben-nowell
Communicated by Binhai Zhu In the popular computer game of Tetris, the player is given a sequence of tetromino pieces and must pack them into a rectangular gameboard initially occupied by a given...
APRIORI, A Depth First Implementation (2003)
We will discuss ##, the depth first implementation of APRIORI as devised in 1999 (see [8]). Given a database, this algorithm builds a trie in memory that contains all frequent itemsets, i.e., all...
Competitive Neural Networks (2002)
For Customer Choice, Walter A. Kosters
In this paper we propose and examine two dierent models for customer choices in for instance a wholesale department, given the actual sales. Both customers and products are modeled by points in a...
Interesting fuzzy association rules in quantitative databases (2001)
Abstract. In this paper we examine association rules and their interestingness. Usually these rules are discussed in the world of basket analysis. Instead of customer data we now study the situation...
Interesting Association Rules in Multiple Taxonomies (2000)
In this paper we study association rules in order to understand customer behaviour. We examine the case where many customers may choose from a long list of products. Suppose that several taxonomies...
A Bayesian Approach to Combined Neural Networks Forecasting (2000)
Maurits D. Out, Walter A. Kosters
. Suitable neural networks may act as experts for time series predictions. The naive prediction is in a Bayesian manner used as prior to steer the weighted combination of these experts. The paper was...
Mining clusters with association rules (1999)
Walter A. Kosters, Elena Marchiori
In this paper we propose a method for extracting clusters in a population of customers, where the only information available is the list of products bought by the individual clients. We use...
Mining Clusters with Association Rules (1999)
Walter A. Kosters, Elena Marchiori
. In this paper we propose a method for extracting clusters in a population of customers, where the only information available is the list of products bought by the individual clients. We use...
Solving 3-SAT Using Adaptive Sampling (1998)
Michiel De Jong, Walter A. Kosters
A framework is briefly introduced, which supports the comparison of Neural Networks and Evolutionary Algorithms. This framework, called "Adaptive Sampling", was used for the design of two...
Fourier Analysis of Genetic Algorithms (1998)
Walter A. Kosters, Joost N. Kok, Patrik Floréen Leiden, Patrik Flor'een
We propose a general framework for Fourier analysis in the field of genetic algorithms. We introduce special functions, analogous to sine and cosine for real numbers, that have nice properties with...
Solving 3-SAT Using Adaptive Sampling (1998)
A framework is briefly introduced, which supports the comparison of Neural Networks and Evolutionary Algorithms. This framework, called "Adaptive Sampling", was used for the design of two...
Maximum Likelihood Weights for a Linear Ensemble of Regression Neural Networks (1998)
Michiel Van, Walter A. Kosters, Joost N. Kok
In this we paper study the problem of combining the outputs of the members of an ensemble of neural networks. We review the commonly used methods and thoroughly derive a cost function from a maximum...
Maximum Likelihood Weights for a Linear Ensemble of Regression Neural Networks (1998)
Walter A. Kosters, Joost. N. Kok
In this we paper study the problem of combining the outputs of the members of an ensemble of neural networks. We review the commonly used methods and thoroughly derive a cost function from a maximum...
Two Neural Network Methods for Multidimensional Scaling (1997)
Michiel Van, Joost N. Kok, Walter A. Kosters
Multidimensional scaling (MDS) embeds points in a Euclidean space given only dissimilarity data. Only very recently MDS has gotten some attention from neural network researchers. We propose two...
Understanding Customer Choice Processes Using Neural Networks (1997)
Walter A. Kosters, Han La Poutré
We propose and examine two different models for customer choices in a wholesale department, given the actual sales. Both customers and products are modelled by points in a k-dimensional real space....
Theory of Genetic Algorithms (1997)
Thomas Back, Joost N. Kok, Walter A. Kosters
this paper we introduce some methods for examining the fundamental properties of genetic algorithms ([Hol75, Jon75, Gol89c, Mit96]), probabilistic search and optimization algorithms that work on...
Theory of Genetic Algorithms (1997)
Thomas Bäck, Joost N. Kok, Walter A. Kosters
this paper we introduce some methods for examining the fundamental properties of genetic algorithms ([Hol75, Jon75, Gol89c, Mit96]), probabilistic search and optimization algorithms that work on...
Two Neural Network Methods for Multidimensional Scaling (1996)
Joost N. Kok, Walter A. Kosters
Multidimensional scaling (MDS) embeds points in a Euclidean space given only dissimilarity data. Only very recently MDS has gotten some attention from neural network researchers. We propose two...
Understanding Customer Choice Processes Using Neural Networks (1996)
We propose and examine two different models for customer choices in a wholesale department, given the actual sales. Both customers and products are modelled by points in a k-dimensional real space....
In this paper we introduce the triangular heap, a heap with the special property that for every father node its right child (if present) is smaller than its left child. We show how triangular heaps...
Expected Heights in Heaps (1992)
In this paper several recurrences and formulas are presented leading to an upper bound and a lower bound, both logarithmic, for the expected height of a node in a heap. These bounds are of interest...
Clustering Improves the Exploration of Graph Mining Results (1970)
Joost N. Kok, Walter A. Kosters
Mining frequent subgraphs is an area of research where we have a given set of graphs, and where we search for (connected) subgraphs contained in many of these graphs. Each graph can be seen as a...