Paulo Martins Engel

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Geographic data preprocessing is the most effort and time consuming step in spatial data mining. In order to facilitate geographic data preprocessing and increase the practice of spatial data mining, this paper presents Weka-GDPM, an interoperable module that supports automatic geographic data preprocessing for spatial data mining. GDPM is implemented into(More)
The paper presents a general framework for concurrent navigation and exploration of unknown environments based on discrete potential fields that guide the robot motion. These potentials are obtained from a class of partial differential equation (PDE) problems called boundary value problems (BVP). The boundaries are generated from sensor readings and(More)
In this paper we introduce RL-CD, a method for solving reinforcement learning problems in non-stationary environments. The method is based on a mechanism for creating, updating and selecting one among several partial models of the environment. The partial models are incrementally built according to the system's capability of making predictions regarding a(More)
This paper presents a new algorithm for unsupervised incremental learning based on a Bayesian framework. The algorithm, called IGMM (for In-cremental Gaussian Mixture Model), creates and continually adjusts a Gaussian Mixture Model consistent to all sequentially presented data. IGMM is particularly useful for on-line incremental clustering of data streams,(More)
The large amount of patterns generated by frequent pattern mining algorithms has been extensively addressed in the last few years. In geographic pattern mining, besides the large amount of patterns, many are well known geographic domain associations. Existing algorithms do not warrant the elimination of all well known geographic dependences since no prior(More)
In frequent geographic pattern mining a large amount o f patterns is well known a priori. This paper presents a novel approach for mining frequent geographic patterns without associations that are previously known as non-interesting. Geographic dependences are eliminated during the frequent set generation using prior knowledge. After the dependence(More)
In this paper we propose a method for solving reinforcement learning problems in non-stationary environments. The basic idea is to create and simultaneously update multiple partial models of the environment dynamics. The learning mechanism is based on the detection of context changes, that is, on the detection of significant changes in the dynamics of the(More)
MOTIVATION With the increase in submission of sequences to public databases, the curators of these are not able to cope with the amount of information. The motivation of this work is to generate a system for automated annotation of data we are particularly interested in, namely proteins related to the Mycoplasmataceae family. Following previous works on(More)