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Given a database, structure mining algorithms search for substructures that satisfy constraints such as minimum frequency, minimum confidence, minimum interest and maximum frequency. Examples of substructures include graphs, trees and paths. For these substructures many mining algorithms have been proposed. In order to make graph mining more efficient, we… (More)

title = {Error-Backpropagation in Temporally Encoded Networks of Spiking Neurons.}, journal = {Neurocomputing}, year = {2002}, pages = {}, note = {To appear; an abstract has appeared in the proceedings of ESANN'2000} } This reprint corresponds to the article " Error-Backpropagation in Temporally Encoded Networks of Spiking Neurons " , This is a preprint of… (More)

Mining frequent subtrees from databases of labeled trees is a new research field that has many practical applications in areas such as computer networks, Web mining, bioinformatics, XML document mining, etc. These applications share a requirement for the more expressive power of labeled trees to capture the complex relations among data entities. Although… (More)

The upgrade of frequent item set mining to a setup with multiple relations —frequent query mining— poses many efficiency problems. Taking Object Identity as starting point, we present several optimization techniques for frequent query mining algorithms. The resulting algorithm has a better performance than a previous ILP algorithm and competes with more… (More)

Given a database of graphs, structure mining algorithms search for all substructures that satisfy constraints such as minimum frequency, minimum confidence, minimum interest and maximum frequency. In order to make frequent subgraph mining more efficient, we propose to search with steps of increasing complexity. We present the GrAph/Sequence/Tree extractiON… (More)

Several algorithms have already been implemented which combine association rules with first order logic formulas. Although this resulted in several usable algorithms, little attention was payed until recently to the efficiency of these algorithms. In this paper we present some new ideas to turn one important intermediate step in the process of discovering… (More)

Recently, an algorithm called Freqt was introduced which enumerates all frequent induced subtrees in an ordered data tree. We propose a new algorithm for mining unordered frequent induced sub-trees. We show that the complexity of enumerating unordered trees is not higher than the complexity of enumerating ordered trees; a strategy for determining the… (More)

We demonstrate that spiking neural networks encoding information in the timing of single spikes are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on real-world data, and we demonstrate how temporal synchrony… (More)

Substructure mining algorithms are important drug discovery tools since they can find substructures that affect physicochemical and biological properties. Current methods, however, only consider a part of all chemical information that is present within a data set of compounds. Therefore, the overall aim of our study was to enable more exhaustive data mining… (More)