José María Peña Sánchez

Integrating RDMS and Data Mining Capabilities Using Rough Sets

Mining information from large databases has been recognized as a key research topic in database systems. The explosive growth of databases has made neccesary to discover techniques and tools to transform the huge amount of stored data, into useful information. Rough Set Theory [17] has been applied since its very beginning to different application areas. This chapter presents an integration of Relational DataBase Management technology with Rough Sets Theory to show how the algorithms can be successfully translated into SQL and used as a powerful tool for knowledge discovery.

Integrating KDD Algorithms and RDBMS Code

In this paper we outline the design of a RDBMS that will provide the user with traditional query capabilities as well as KDD queries. Our approach is not just another system which adds KDD capabilities, this design is aimed to integrate these KDD capabilities into RDBMS core. The approach also defines a generic engine of Data Mining algorithms that allows easy enhancement of system capabilities as a new algorithm is implemented.

A contactless identification system based on hand shape features

This paper aims at studying the viability of setting up a contactless identification system based on hand features, with the objective of integrating this functionality as part of different services for smart spaces. The final identification solution will rely on a commercial 3D sensor (i.e. Leap Motion) for palm feature capture. To evaluate the significance of different hand features and the performance of different classification algorithms, 21 users have contributed to build a testing dataset. For each user, the morphology of each of his/her hands is gathered from 52 features, which include bones length and width, palm characteristics and relative distance relationships among fingers, palm center and wrist. In order to get consistent samples and guarantee the best performance for the device, the data collection system includes sweet spot control; this functionality guides the users to place the hand in the best position and orientation with respect to the device. The selected classification strategies - nearest neighbor, supported vector machine, multilayer perceptron, logistic regression and tree algorithms - have been evaluated through available Weka implementations. We have found that relative distances sketching the hand pose are more significant than pure morphological features. On this feature set, the highest correct classified instances (CCI) rate (>96%) is reached through the multilayer perceptron algorithm, although all the evaluated classifiers provide a CCI rate above 90%. Results also show how these algorithms perform when the number of users in the database change and their sensitivity to the number of training samples. Among the considered algorithms, there are different alternatives that are accurate enough for non-critical, immediate response applications.

Using the Apriori Algorithm to Improve Rough Sets Results

Ever since Data Mining first appeared, a considerable amount of algorithms, methods and techniques have been developed. As a result of research, most of these algorithms have proved to be more effective and efficient. For solving problems different algorithms are often compared. However, algorithms that use different approaches are not very often applied jointly to obtain better results. An approach based on the joining of a predictive model (rough sets) together with a link analysis model (the Apriori algorithm) is presented in this paper.

3D dendritic spine automatic detection and segmentation through samples obtained by confocal microscopy

Dendritic spines are a small protrusions from a neurons dendrite that typically receives input from a single synapse of an axon. We propose an automatic method to obtain dendritic spines parameters, in terms of length, volume, angles and density.

Simulation of the release and diffusion of neurotransmitters in neuronal synapses: Analysis and modelling

Chemical synaptic transmisssion involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. We have developed synaptic models that had a simple geometry as far as shape is concerned, but had a variable set of parameters that specified the dimensions of the structures involved in the synaptic junction. We have analyzed simulations based on these idealized models of excitatory synapses where AMPA receptors are present and the neurotransmitter involved is glutamate.

Parallel Data Mining Experimentation Using Flexible Configurations

When data mining first appeared, several disciplines related to data analysis, like statistics or artificial intelligence were combined toward a new topic: extracting significant patterns from data. The original data sources were small datasets and, therefore, traditional machine learning techniques were the most common tools for this tasks. As the volume of data grows these traditional methods were reviewed and extended with the knowledge from experts working on the field of data management and databases. Today problems are even bigger than before and, once again, a new discipline allows the researchers to scale up to these data. This new discipline is distributed and parallel processing. In order to use parallel processing techniques, specific factors about the mining algorithms and the data should be considered. Nowadays, there are several new parallel algorithms, that in most of the cases are extensions of a traditional centralized algorithm. Many of these algorithms have common core parts and only differ on distribution schema, parallel coordination or load/task balancing methods. We call these groups algorithm families. On this paper we introduce a methodology to implement algorithm families. This methodology is founded on the MOIRAE distributed control architecture. In this work we will show how this architecture allows researchers to design parallel processing components that can change, dynamically, their behavior according to some control policies.

Rough Dependencies as a Particular Case of Correlation: Application to the Calculation of Approximative Reducts

Rough Sets Theory provides a sound basis for the extraction of qualitative knowledge (dependencies) from very large relational databases. Dependencies may be expressed by means of formulas (implications) in the following way: