Mining Exceptional Social Behaviour

Essentially, our lives are made of social interactions. These can be recorded through personal gadgets as well as sensors adequately attached to people for research purposes. In particular, such sensors may record real time location of people. This location data can then be used to infer interactions, which may be translated into behavioural patterns. In this paper, we focus on the automatic discovery of exceptional social behaviour from spatio-temporal data. For that, we propose a method for Exceptional Behaviour Discovery (EBD). The proposed method combines Subgroup Discovery and Network Science techniques for finding social behaviour that deviates from the norm. In particular, it transforms movement and demographic data into attributed social interaction networks, and returns descriptive subgroups. We applied the proposed method on two real datasets containing location data from children playing in the school playground. Our results indicate that this is a valid approach which is able to obtain meaningful knowledge from the data.This work has been partially supported by the German Research Foundation (DFG) project “MODUS” (under grant AT 88/4-1). Furthermore, the research leading to these results has received funding (JG) from ESRC grant ES/N006577/1. This work was financed by the project Kids First, project number 68639

Livro Verde dos Montados

O Livro Verde dos Montados apresenta diversos objectivos que se interligam: Em primeiro lugar, o Livro Verde pretende reunir e sistematizar, de uma forma simples e acessível ao público, o conhecimento produzido em Portugal pelos investigadores e técnicos de várias instituições de investigação ou de gestão que estudam o Montado. Assume-se como uma oportunidade de caracterizar o sistema tendo em conta as suas várias dimensões, identificando as principais ameaças à sua preservação assim como os caminhos que podem ajudar à sua sustentabilidade. Não sendo um documento científico, baseia-se no conhecimento científico e pretende constituir a base para uma plataforma de organização, tanto dos investigadores como do conhecimento científico actualmente produzido em Portugal sobre o Montado.Em segundo lugar, o Livro Verde deverá contribuir para um entendimento partilhado do que é o Montado, por parte do público, de técnicos e de especialistas, conduzindo a uma classificação mais clara do que pode ser considerado Montado e de quais os tipos distintos de Montados que podem ser identificados. Em terceiro lugar, o Livro Verde estabelece as bases para uma estratégia coordenada de disponibilização de informação sobre o sistema Montado, visando o seu conhecimento, apreciação e valorização pela sociedade portuguesa no seu conjunto. Deste modo, o Livro Verde poderá constituir um instrumento congregador e inspirador para a realização de acções de sensibilização e informação sobre o Montado. Em quarto lugar, pretende-se que o Livro Verde contribua para um maior reconhecimento e valorização do Montado como sistema, a nível do desenho das políticas nacionais por parte dos vários sectores envolvidos.Finalmente, o Livro Verde constituirá um documento parceiro do Livro Verde das Dehesas, produzido em Espanha em 2010, de forma a reforçar o reconhecimento e a devida valorização destes sistemas silvo-pastoris no desenho das estratégias e políticas relevantes pelas instituições europeias. Em suma, os autores pretendem que o Livro Verde dos Montados se afirme como o primeiro passo para uma efectiva definição e implementação de uma estratégia nacional para os Montados

Label Ranking datasets

Real-world Label Ranking datasets

Label Ranking datasets

Real-world Label Ranking datasets

Entropy-based discretization methods for ranking data

Label Ranking (LR) problems are becoming increasingly important in Machine Learning. While there has been a significant amount of work on the development of learning algorithms for LR in recent years, there are not many pre-processing methods for LR. Some methods, like Naive Bayes for LR and APRIORI-LR, cannot handle real-valued data directly. Conventional discretization methods used in classification are not suitable for LR problems, due to the different target variable. In this work, we make an extensive analysis of the existing methods using simple approaches. We also propose a new method called EDiRa (Entropy-based Discretization for Ranking) for the discretization of ranking data. We illustrate the advantages of the method using synthetic data and also on several benchmark datasets. The results clearly indicate that the discretization is performing as expected and also improves the results and efficiency of the learning algorithms

“Want to come play with me?” Outlier subgroup discovery on spatio-temporal interactions

Our lives are made of social interactions which can be recorded through personal gadgets as well as sensors capturing ubiquitous and social data. This type of data, such as spatiotemporal data from the real-time location of people, for example, can then be used for inferring interactions which can be translated into behavioural patterns. In this paper, we consider the automatic discovery of exceptional social behaviour from spatio-temporal interaction data, focusing on two areas: exceptional subgroups and spatiotemporal outliers – both in the form of descriptive patterns. For that, we propose a method for exceptional social behaviour discovery, combining subgroup discovery and network science methods for identifying behaviour that deviates from the norm. We also propose the use of two outlier detection metrics for identifying outliers, namely the Local Outlier Factor (LOF) and the Voronoi area. We applied the proposed method on synthetic data as well as two real datasets containing location data from children playing in the school playground. Our results indicate that this is a valid approach which is able to obtain meaningful knowledge from the data

Ensemble Clustering for Novelty Detection in Data Streams

In data streams new classes can appear over time due to changes in the data statistical distribution. Consequently, models can become outdated, which requires the use of incremental learning algorithms capable of detecting and learning the changes over time. However, when a single classification model is used for novelty detection, there is a risk that its bias may not be suitable for new data distributions. A solution could be the combination of several models into an ensemble. Besides, because models can only be updated when labeled data arrives, we propose two unsupervised ensemble approaches: one combining clustering partitions using the same clustering technique; and other using different clustering techniques. We compare the performance of the proposed methods with well known novelty detection algorithms. The methods were tested on datasets commonly used in the novelty detection literature. The experimental results show that proposed ensembles have competitive performance for novelty detection in data streams

Exceptional Attributed Subgraph Mining To Understand The Olfactory Percept

International audienceHuman olfactory perception is a complex phenomenon whose neural mechanisms are still largely unknown and novel methods are needed to better understand it. Methodological issues that prevent such understanding are: (1) to be comparable, individual cerebral images have to be transformed in order to fit a template brain, leading to a spatial imprecision that has to be taken into account in the analysis; (2) we have to deal with inter-individual variability of the hemodynamic signal from fMRI images which render comparisons of individual raw data difficult. The aim of the present paper was to overcome these issues. To this end, we developed a methodology based on discovering exceptional attributed subgraphs which enabled extracting invariants from fMRI data of a sample of individuals breathing different odorant molecules.Four attributed graph models were proposed that differ in how they report the hemody-namic activity measured in each voxel by associating varied attributes to the vertices of the graph. An extensive empirical study is presented that compares the ability of each modeling to uncover some brain areas that are of interest for the neuroscientists

Seminário de Dissertação (2024)

Página da disciplina de Seminário de Dissertação (MPPP, UFPE, 2022) Lista de participantes == https://docs.google.com/spreadsheets/d/1mrULe1y04yPxHUBaF50jhaM1OY8QYJ3zva4N4yvm198/edit#gid=